Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology
Authors: Saghir Alfasly
Wataru Uegami
Md. Enamul Hoq
Ghazal Alabtah
Hamid Tizhoosh
Venue: NeurIPS 2025 poster ()
Keywords: Histopathology
Generative models
Diffusion models
Image synthesis
Heterogeneous tissue
Self-supervised diffusion model
Primary area: applications
TL;DR: Novel visually prompted diffusion model generates spatially controlled histopathology images, extends to unannotated TCGA, and produces synthetic training data that outperforms real data for segmentation models.

Abstract: Synthetic data generation in histopathology faces unique challenges: preserving tissue heterogeneity, capturing subtle morphological features, and scaling to unannotated datasets. We present a latent diffusion model that generates realistic heterogeneous histopathology images through a novel dual-conditioning approach combining semantic segmentation maps with tissue-specific visual crops. Unlike existing methods that rely on text prompts or abstract visual embeddings, our approach preserves critical morphological details by directly incorporating raw tissue crops from corresponding semantic regions. For annotated datasets (i.e., Camelyon16, Panda), we extract patches ensuring 20-80% tissue heterogeneity. For unannotated data (i.e., TCGA), we introduce a self-supervised extension that clusters whole-slide images into 100 tissue types using foundation model embeddings, automatically generating pseudo-semantic maps for training. Our method synthesizes high-fidelity images with precise region-wise annotations, achieving superior performance on downstream segmentation tasks. When evaluated on annotated datasets, models trained on our synthetic data show competitive performance to those trained on real data, demonstrating the utility of controlled heterogeneous tissue generation. In quantitative evaluation, prompt‐guided synthesis reduces Fréchet Distance by up to 6× on Camelyon16 (from 430.1 to 72.0) and yields 2–3× lower FD across Panda and TCGA. Downstream DeepLabv3+ models trained solely on synthetic data attain test IoU of 0.71 and 0.95 on Camelyon16 and Panda, within 1–2% of real‐data baselines (0.72 and 0.96). By scaling to 11,765 TCGA whole‐slide images without manual annotations, our framework offers a practical solution for an urgent need for generating diverse, annotated histopathology data, addressing a critical bottleneck in computational pathology.

Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology
Saghir Alfasly Wataru Uegami MD Enamul Hoq Ghazal Alabtah
H.R. Tizhoosh"
KIMIA Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN 55901
(Alfasly. Saghir, Tizhoosh. Hamid] emayo. Cmayo.. edu https://lsmiialabmmayo git.ub.....bbb....... teuub.oo/hteeee io/hetero..... tissue tissue_diffuse diiffuee__aaeeee
Abstract
Synthetic data generation in histopathology faces unique challenges: preserv- ing tissue heterogeneity, capturing subtle morphological features, and scaling unannotated datasets. We present a latent diffusion model that generates realistic heterogeneous histopathology images through novel cunl-conditioning approach combining semantic segmentation maps with tissue-specific visual crops. Unlike existing methods that rely text prompts abstract visual embeddings, approach preserves critical morphological details by directly incorporating raw tissue crops from corresponding semantic regions. For annotated datasets (i.e., Camelyon16 16, Panda), we extract patches ensuring 20 80% tissue heterogeneity. For unannotated data (i.e.. TCGA), we introduce self-supervised extension that clusters whole-slide images into 100 tissue types using foundation model embed- dings, automatically generating pseudo-semantic maps for training. Our method synthesizes high-fidelity images with precise region-wise annotations, achieving superior performance on downstream segmentation tasks. When evaluated on anno- tated datasets, models trained on our synthetic data show competitive performance those trained on real data, demonstrating the utility of controlled heterogeneous tissue generation. In quantitative evaluation, prompt-guided synthesis reduces Fréchet Distance by up to 6x on Camelyon 16 (from 430.1 to 72.0) and yields 3 lower FD across Panda and TCGA, Downstream Deepl -ab33++ models trained
2 3-3 solely on synthetic data attain test IoU of 0.71 and 0.95 on Camelyon 16 and Panda, within 1 2% of real-data baselines (0.72 and 0.96). By scaling to 11, 765 TCGA whole-slide images without manual annotations, our framework offers a practical solution for urgent need for generating diverse, annotated histopathology data, addressing a critical bottleneck in computational pathology.
Introduction
Histopathology image analysis forms the cornerstone of cancer diagnosis, yet remains constrained
by data scarcity, laborious annotation processes, and privacy concerns [8, 24, 29, 1, 21J. While generative Al has transformed natural image synthesis, its application to histopathological imaging in digital pathology faces unique challenges due the complex, multi-scale architecture of biological tissues and the critical importance of preserving diagnostically relevant features [44, 16, 2]. Most of the current approaches have predominantly focused generating homogeneous tissue types using text-based prompting systems, which introduce significant interobserver variability and limit clinical utility, particularly problematic domain where expert agreement is already inconsistent [15, 42].
The evolution from GAN-based methods to diffusion models has improved image quality and training stability [13, 18), but existing frameworks fail to account for the heterogeneous nature of real-
"Conresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Reference Semantic Map(SM)
SM Only
SM+Embbddinggs
SM+Crops
Figure 1: Comparison of reference and synthetic Camelyon16 patches using three conditioning schemes. From left to right: original histopathology patch; binary tumor-normal mask; generated image with a semantic segmentation map conditioning only; synthetic image by combining conditions of semantic maps and abstract embeddings; synthetic output of our model conditioned the semantic map and tissue-specific visual crop prompts. The crop- crop-guided guided generation recovers fine morphological details and staining heterogeneity more faithfully than embedding-based conditioning.
world histopathology samples. Levine et al. [28] demonstrated that GANs could generate images indistinguishable from real histopathology samples, while Moghadam et al. [30] marked the transition to diffusion models with superior image quality and training stability. Despite these advances, clinical specimens typically contain multiple tissue types and pathological features within a single slide, requiring region-specific control generation parameters. This limitation severely restricts the utility synthetic data for developing robust diagnostic algorithms that must recognize complex patterns in diverse tissue regions.
Further complicating this landscape is the tension between the global architecture the tissue and the local cellular details. Traditional generative approaches struggle to maintain consistency across multiple magnification levels while preserving the fine- aeeeggraied morphological features critical for diagnosis [34, 41, 2]. Recent work, URCDM [10], has addressed this through cascaded diffusion models generating images at multiple resolutions simultancously, while DiffInfinite [4] enables arbitrary-size image synthesis with preserved long-range structural correlations. However, this multi- scale challenge remains particularly acute when attempting to synthesize realistic transitions between different tissue types, capability essential for training segmentation algorithms and supporting differential diagnosis.
Our work addresses these challenges by introducing a visually prompted latent diffusion model designed specifically for heterogeneous tissue synthesis in histopathology. Unlike text-guided approaches, framework leverages spatial masks and visual exemplars provide fine-graid fine-grained control over region- speciifc generation. Previous work, in the literature, NASDM [39] and subsequent extensions by Konz et al. [25] and Xu et al. [47] demonstrated the value of region-guided generation and semantic instance masks. Our method expands this concept to enable the synthesis of complex, multi-tissue samples with realistic transitions and clinically relevant features shown in Figure 1. Our approach significantly reduces the annotation burden while maintaining high fidelity to real-world pathological presentations 2. The main contributions of our work include:
New Dual-Conditioning Architecture for Histopathology Synthesis. We developed a unique visual crop-guided diffusion model that combines semantic maps with tissue exemplars, preserving critical morphological features (nuclear texture, staining patterns, cellular structure) that lost in text-based embedding- approaches. This ables precise region-specific control while maintaining authentic tissue appearance for heterogeneous sample generation.

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Self-Supervised Framework for Unannotated Whole-Slide Images. We introduced a scalable solution for the TCGA dataset (11.765 WSIs) that automatically discovers and clusters 100 distinct tissue phenotypes without manual annotation. This democratizes access to diverse synthetic data across 33 cancer types while preserving patient privacy and addressing critical data scarcity in computational pathology.
Comprehensive Multi-Modal Validation Framework. We established a rigorous evalua- tion pipeline combining quantitative metrics (Fréchet distance across 8 foundation model encoders) with downstream task performance (segmentation IoU scores). Most notably, our blinded assessment by certified pathologists using a 5-point Likert scale across multiple quality criteria revealed that our synthetic images were indistinguishable from real samples, with one pathologist commenting: "The generated images tended have equal or higher quality than the real images."
Through rigorous evaluation involving both quantitative metrics (including the Fréchet Inception Dis- tance [6]) and expert pathologist evaluation, we demonstrate that our approach generates histopathol- ogy images that are indistinguishable from real samples while providing unprecedented control over tissue composition. Synthetic datasets generated using our method effectively augment or replace real data in training diagnostic models, addressing the critical issue of data scarcity while preserving patient privacy.
By enabling the generation of diverse, annotated histopathology datasets without requiring patient data sharing, our framework represents significant step toward more equitable and robust AI development in computational pathology [27]. This capability is particularly valuable for rare cancer types and underrepresented populations, potentially democratizing access to high-quality training data between institutions, regardless of their size or resources.
2 Related Work
Generative Models in Histopathology. Visual generative models have evolved from early GANs [17] to sophisticated diffusion models [18], with recent advances like ControlNet [49] enabling fine-
grained control. Adapting these to histopathology presents unique challenges due to the complexity
of the tissue and the diagnostic significance of subtle morphological features. Although early GANs demonstrated feasibility [28, 34, 2], recent diffusion- based approaches show superior quality [30]. Domain-specific methods [26. 23, 51, 10] have emerged, with URCDM [10] addressing multi- resolution synthesis and enabling arbitrary-size generation [4, 43J. However, most approaches generate homogeneous tissue types, limiting their utility for training diagnostic models that require heterogeneous tissue representations, limitation that our framework specifically addresses.
Conditioning Mechanisms for Histopathology Synthesis. Existing conditioning approaches fall into three categories, each with significant limitations. Unconditioned models [44, 16] produce realistic images but lack control over tissue types and pathological features, severely limiting their utility for training task specific models. Metadata dettadata-uided,, textegu or mask-guided models [14, 48, 47, 9] suffer from interobserver variability, as documented by Elmore et al. [15] who found substantial disagreement among pathologists (kappa values as low as 0.48). Visual embedding or RNA-seq embedding approaches [33, 12, 50] avoid text ambiguity, but introduce lossy transformations that can obscure critical diagnostic features. Even domain- speciiic embeddings suffer from information loss during dimensionality reduction. Our approach circumvents these limitations by directly conditioning on real tissue crops combined with semantic maps, preserving original visual characteristics without intermediate representations.
Semantic Map-Based Generation. Recent works have explored semantic map conditioning for precise spatial control. Shrivastava and Fletcher [39] pioneered this with NASDM, while Konz et al. [25] extended through random mask ablation. Although spatially accurate, these approaches typ- ically focus single tissue types or cellular structures rather than heterogeneous tissue architectures. Our dual-condition mechanism combines semantic maps with visual crops, enabling the synthesis of diverse tissue compositions while maintaining both spatial accuracy and morphological fidelity, essential for generating comprehensive segmentation datasets.
Large-Scale Synthesis and Evaluation. Whole-slide image (WSI) synthesis presents unique challenges due to gigapixel resolution and structural dependencies. Cechnicka et al. [10] and Aversa et al. [4] addressed scale issues through cascaded models and infinite tiling, respectively, but struggled to maintain segmentation accuracy with visual characteristics. Our self-supervised framework in
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(a) Unsupervised Tissue Clustering
(b) Online Region/Mask Sampling
-1IK WEls
-634 million
Deep
Pseudo- labeled patches
WSHlevel segment maps
patches
Regions/ Maps
Histo
Foundation
model
100
Conditioning
Semantic
E
Forward Diffusion Process
Zn
Repeat
Zt
Xo
Visual tissue prompts (crops)
UNet
t=1
t=T+1
UNet
t=T
Z1
Z1+1
Zt
X
(c) Diffusion Model
Figure 2: Schematic overview of the Hetero TeserooTssss-iffieeii framework for heterogeneous tissue synthesis in histopathology. (a) Unsupervised Tissue Clustering: For unannotated datasets (TCGA), approximately 11, 765 whole-slide images (WSIs) are processed to extract 634,435, 133,133 million patches. A histopathology foundation model extracts deep features, which are used to cluster patches into 100 distinct tissue types, creating pseudo-labeled data. (b) Online Region/Mask Sampling: The pseudo-labeled patches are used to generate WSI-level segmentation maps and regional masks for conditioning the diffusion model. (c) Diffusion Model: Our cuul-conditioning approach combines semantic maps with visual tissue prompts (crops) to guide the latent diffusion process. The model encodes the imput image to the latent space, applies forward diffusion to create noisy latent, then reverses this process with UNet denoising conditioned on both semantic maps and visual tissue exemplars. For annotated datasets (Camelyon16 [5] and Panda [7]), only component (c) is used with their existing semantic maps.
this paper, trained TCGA's 11,765 diagnostic WSIs, generates 100 distinct tissue types while preserving region-specific control through dual conditioning.
Evaluation of synthetic histopathology requires specialized metrics beyond standard FID and IS, which use networks pretrained on natural images. Domain-specific alternatives include Fréchet Distance and Topological Fréchet Distance [47], complemented by the evaluation of expert patholo- gists [40]. Our comprehensive validation combines these metrics with downstream task performance, the ultimate measure of the utility of synthetic data.
Our work advances the field through three key innovations. First, we avoid intermediate repre- sentations that compromise fidelity by directly conditioning on visual crops from real histopathology images combined with spatial semantic maps. Unlike semantic-only methods [39, 25], text- based approaches [36], embedding-benl
techniques [31, 35], our approach preserves -peecifc attributes (i.e., texture, cellular morphology, and staining patterns) that abstract representations lose. This direct visual conditioning enables the synthesis of realistic heterogeneous samples with precise spatial control, which is essential to train robust diagnostic models. Second, our self- extension to TCGA democratizes access to diverse synthetic data across cancer types, generating 100 distinct tissue phenotypes without manual annotation while addressing critical data scarcity and preserving patient privacy [27]. Third, we establish comprehensive validation through rigorous met- rics combining expert pathologist assessment with quantitative measures (Fréchet distance, precision,

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recall, Fl score), and demonstrate utility through downstream segmentation tasks that confirm the high quality of our generated annotated data.
3 Method
Our approach addresses the fundamental challenge in histopathology synthesis: generating realistic heterogeneous tissue images with precise region-based control while preserving morphological fidelity. We achieve this through novel dual-conditioning latent diffusion model that combines semantic maps with tissue-specific visual crops.
3.1 Preliminaries: Latent Diffusion Models
Algorithm I: Heterogeneous Patch Sampling for Annotated Data
Latent diffusion models (LDMs) [36] offer an efficient framework for high-quality image syn- thesis by operating in a compressed latent space. Given an image To E Rxx encoder & maps it to lower-dimensional representä-
Require: Dutaset with patches and segmestation masks
Reguire:
Tioar mioo houds ["min: Pmaae] = [0.2 20.2,00
Reguire:
(fmins danssl [50, 200 Ensure: Training sample (#te)
Crop
tion 2g (xx)) hxxxxx where generally & H and w < W.
1: function WwT.HHHOONNNNNOPPPPPTS
2:
(e. M) e- SelectPancs (D) where tissue ratios E [Fmmta Panxx]
3:
K <- namber of tisme classes
The forward diffusion process gradually cor- rupts 20 by adding Gaussian noise over time steps:
4.
taminlize C e- zorost W. 3.)
5:
= Ko
6:
Cl., (k 1)] + M M R)
> Semantic channel Class & preseat
7:
I MfG,, 4]
8:
Randomlatt fmnax)
(((12=1))= N 112-,, (1)
9:
(r) ExtnctSqquaeCiooge. Mi.. k).
10:
+- Angmentipna)
P Opeional rocaionniii
where (3x)7- is predefined variance schedule. This can be expressed in closed form as
11:
C2 serost W.
12:
Calr d.
13:
Cha
2 20 vI âie, ~(0,1 (2)
14:
end
15:
end for
where āg I (1 B,).
16:
(x, 17:
end function
A neural network cg learns to reverse this process by predicting the noise component, optimizing
LLDM = Ez,,t.c [|| (0,,, c)
where represents optional conditioning information.
Existing histopathology synthesis methods typically depend class labels, text descriptions, or global feature embeddings. However, these approaches struggle with two critical requirements: (1) precise spatial control over tissue types and (2) preservation of fine-grained morphological details such as nuclear texture and staining patterns.
3.2
Our Dual-Conditioning Approach
We introduce PesteroTissue-Diffuu (HTD), which fulfills both requirements through new dual- conditioning mechanism. Our key insight that semantic maps provide spatial precision while tissue-specific visual crops preserve morphological authenticity.
3.2.1 Dual Conditioning Formulation
Given histopathology patch E H W x3 and its semantic segmentation map M E {0, X (where denotes tissue classes), we construct our conditioning signal as detailed Alg.
e = concat M, C1,..., Ck);
where each C; E Xxx33 sparse visual crop tensor for tissue class The construction process involves:
1. Extract square crop p: of size d x d (where d {50, 200} pixels) from region labeled as class i Initialize zero tensor C, matching the full patch dimensions 3. Place p: at random coordinates within the semantic region defined by M;
This design preserves tissue- specific attributes (texture, cellular morphology, staining) that are lost in abstract representations while maintaining spatial correspondence with the semantic map.
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3.3 Self-Supervised Extension for Unannotated WSIs
A critical challenge in histopathology synthesis is the lack of pixel-wise annotations at scale. While datasets like 6amelyon16 provide detailed segmentation, they cover limited number of tissue types and cancer subtypes. The Cancer Genome Atlas (TCGA), containing over 11, 765 whole-slide images across 33 cancer types. offers unprecedented diversity but lacks segmentation annotations. Our self-supervised extension bridges this gap by automatically discovering tissue phenotypes and creating -anottaannttsiions that enable HTD training on this extensive public resource.
3.3.1 Tissue Type Discovery via Deep Clustering
Our approach leverages the semantic richness of foundation models pre-trained on histopathology data. These models learn representations that naturally cluster similar tissue types, which we exploit for unsupervised tissue discovery. The process involves three carefully designed phases that balance computational efficiency with comprehensive tissue representation.
In the strategic feature extraction phase, we process each WSI w E W by extracting non-overlapping patches at the highest available magnification of each WSI. After applying tissue detection to exclude background regions, we compute features (p) for each patch p using foundation model such as UNI [1]]. It took3 months to extract the entire TCGA 224 x 224 patch embeddings (i.e., 634, 435, 134 patches) of the high magnification of each WSI on NVIDIA A100 GPUs with 80GB, To ensure diversity while maintaining computational tractability, we strategically sample N = 1000 patches per WSI using diversity aware sampling strategy given as
Paamle = DiversitySample (Pe, N, spatiallweightt = 0.3, feature feature_weight = 0.7). (5)
where spatial weighting ensures coverage across the WSI and feature weighting promotes phenotypic diversity. This approach prioritizes edge cases and underrepresented regions that might contain rare but clinically significant tissue types.
The hierarchical clustering phase employs a two-stage approach to discover tissue phenotypes. Initially, we apply k-means clustering with K 100 clusters on the collected features from all sampled patches across the dataset. Subsequently, for clusters exhibiting high intra-cluster variance, we perform sub-clustering to identify subtle phenotypic variations. This hicrarchical approach captures both major tissue categories such as tumor, stroma, and necrosis, well as finer distinctions such different grades of tumor differentiation or varying inflammatory patterns.
For the multi-scale semantic map generation, we create representations at multiple granularities for each WSI as
S₁ AssignCCluseer(,, Cx) Patches(w)),
(6)
where k E {5, 10, 20, 50, 100} represents different levels of tissue granularity. This multiscale representation enables HTD to learn both coarse tissue boundaries and fine-grained morphological variations, adapting to the complexity of different tissue regions within the same slide.
3.3.2 Adaptive Heterogeneous Region Sampling
Our TCGA sampling strategy guarantees tissue heterogeneity while maintaining computational efficiency. We introduce an adaptive framework to ensure every training sample contains meaningful tissue diversity, avoiding homogeneous regions that fail to capture critical tissue interactions.
We compute heterogeneity maps for each WSI using entropy to quantify tissue diversity. For region r with cluster distribution, the heterogeneity score is given as
H(r) = Σ p((r) logp((r,,
where (r) represents the proportion of cluster i in region r. This identifies regions with rich tissue interactions like tumor-stroma interfaces.
there are insufficient heterogeneous regions in the current granularity, the algorithm adapts by decreasing the size of the region increasing the granularity of the cluster k. ensuring that every sampled region contains at least two distinct tissue types.
For selected regions, we construct multi-scale visual crops with dimensions adapting to tissue complexity:
d: drase (1 + ComplexityScore()).
(8)

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Complex tissues receive larger crops to capture full morphology. Strategic placement maximizes information by centering on representative regions for homogeneous clusters and sampling boundaries for heterogeneous ones.
Tissue-aware augmentations include stain variations for batch effects, controlled rotations respecting tissue orientation, and brightness adjustments mimicking scanner variations. Dynamic cluster granularity follows curriculum learning:
k' (t) Kmin + (Kmax - kmin) min (1, waam/Ta
This progression from coarse to fine tissue distinctions prevents early overfitting while ensuring full phenotypic coverage. Complete algorithms for sampling and clustering. along with detailed implementation specifications, are provided in the supplementary materials.
3.4 Tissue Classifier in Inference Phase
To enhance computational efficiency during inference, lightweight tissue classification model was implemented following the clustering of TCGA images. While the initial clustering utilized computationally intensive foundation models (UNI in case) generate embeddings, applying this same approach during inference would create a significant computational bottleneck. Instead, a more efficient ViT-small architecture was trained directly the pseudo-labeled clusters, enabling rapid tissue type classification without requiring foundation model embedding extraction or cen- troid matching. This classifier processes 224x 224 visual crop inputs and directly predicts cluster assignment from the 100 identified tissue types, reducing inference computational requirements by approximately 85% compared to the original embedding-bant -baed approach. The model was trained on 514,029 patches extracted from 11, 765 diagnostic TCGA WSIs using Adam W optimization with learning rate le 3, achieving 47% accuracy the held-out test set. This approach substantially streamlines the inference pipeline while maintaining classification fidelity, enabling practical de- ployment in eesource-consrrimg environments. More details of this cluster classifier training and implementation are provided in the supplementary file.
Overall, method uniquely combines the spatial precision of semantic maps with the morphological authenticity of visual crops, creating a dunl-cooniiioning approach that addresses key limitations of existing methods. Unlike text-based conditioning, unlike global feature conditioning, we preserve fine-grained tissue characteristics; and unlike semantic- only approaches, capture staining variations and cellular details. The self-supervised extension to TCGA demonstrates scalability to massive unannotated datasets, opening possibilities for diverse tissue synthesis across cancer types.
avoid ambiguity and inter-observer variability;
4 Results
Quantitative Results Fidelity Fréchet Distance (FD). We evaluated the fidelity of generated histopathology images using Fréchet Distance (FD) across multiple foundation model encoders on CAMELYONI6, PANDA, and TCGA datasets (Table 1). The results demonstrate that prompt conditioning significantly improves generation quality compared to nonprompt (NP) baseline across all datasets. Notably, RN50-BT shows the most dramatic improvement, with FD scores decreasing from 430.1 to 72.0 on CAMELYON16 when using prompts a 6-fold reduction. Similarly, DINOv2 and UN12 encoders exhibit substantial improvements with prompt conditioning, achieving 2-3x lower FD scores. The embedding prompt approach shows intermediate performance, suggesting that direct visual-crop prompts provide more effective semantic guidance than crop-based embeddings. Interestingly, the improvement magnitude varies across datasets, with PANDA showing the most consistent gains across all encoders. These findings validate that semantic conditioning through prompts enables more faithful tissue structure generation, with certain encoder architectures (RN50- BT, DINOv2) being particularly responsive to textual guidance. Comprehensive ablation studies, per-class FD analysis, and architectural comparisons are provided in the supplementary materials.
Downstream Evaluation Tissue Segmentation. We evaluated synthetic datasets tissue segmentation tasks using DeepLabv3+ on Camelyon 16 and Panda datasets, as shown in Table 2 and Figure 3.
The results demonstrate a significant milestone for generative models in medical imaging: Syn- thetic data with proper conditioning achieve segmentation performance remarkably close to real
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Table 1: FD Results for visual-crop Prompt, Nonprompt, and crop embedding prompt conditions
across CAMELYONI6 [5], PANDA [7]. and TCGA [45] datasets
Dotaset
Cond.
Lumit-
[22]
Gigarath
[46]
1-Optimus-0
[351
PathDiio
13]
RNSGBT
122]
DINO22
[32]
UNI-11
[111
UNI
[1]]
NP 1360.9
714.0
713.9
7540.6
43011
122.0
135,8
70.01
CAM16
Emi Pronpe
991
696.6
664.7
4331
183.
280.6
141.6
641.1
Visual Prompt
620
353,0
425.2
2591
72.0
52.7
85.2
481.4
PANDA
Visual Prompt
NP
X77.8
532.2
199.7
422.2
227.3
5124.7
2230.9
1500
352.4
630.5
4306.7
Prompt
521.4
Text kg
Toxt kd
Table Test IoU perfor- mance of DeepLaby3+ trained real and synthetic data vari- ants across Camelyon16 and Panda datasets.
2
Data
Caml6
Panda
NoPrompt
0.63
0.86
Deeplaabb2 performance realayysthetic [Camelyonii)
malleyythetic
PromptEmbed
0.69
0.88
Figure 3: Validation and test IoU comparison of DeepLabv3+ models
Visual Prompt
0.71
0.95
across different training dataset types on (a) Camelyon16 and (b) Panda. Training synthetic data generated with visual prompt achieves performance comparable to real data training, with NoPrompt showing lower performance.
Real
0.72
0.96
data. Specifically, Prompt-based synthetic training achieved test IoU scores 0.71 and 0.95 on 1 and PANDA, respectively, compared to 0.72 and 0.96 with real data training, a gap of merely 1-2%. This near-parity performance particularly noteworthy as our objective extends beyond data augmentation to complete replacement of real patient data, addressing critical privacy concerns in medical AI development. The inclusion of visual-crop prompts or prompt embeddings proves essential, as NoPrompt synthetic data shows more substantial performance drop (0.63 and 0,86), highlighting the importance of semantic guidance in generating task-relevant synthetic samples. These findings suggest that carefully conditioned generative models can produce training data of sufficient quality to potentially eliminate the need for real patient data when developing robust segmentation models, representing crucial advancement toward prraccciees medical AI. Additional experimental results, ablation studies, and cross-dataset generalization analyses are provided in the supplementary materials.
Qualitative Evaluation Certified Pathologist Assessment. complement the quantitative metrics and downstream task performance, comprehensive pathologist evaluation was conducted assess the clinical realism and diagnostic utility of synthetic images. The evaluation employed a blinded assessment framework where expert pathologists reviewed 120 randomly selected images from both real and synthetic datasets without prior knowledge of their origin. The assessment interface in Figure S6, web application presented pathologists with five evaluation criteria: overall image quality, histological structural detail, nuclear morphology accuracy, presence of artifactual hallucinations, and final determination of image authenticity. Each quality metric was rated on a 5-point Likert scale, while hallucination presence and real/synthetic classification were binary assessments. The evaluation protocol encompassed images from three datasets (CAMELYON16. PANDA, and TCGA), with equal representation of real and synthetic samples to ensure an unbiased assessment. After collecting responses from the certified pathologist, statistical analysis was performed to quantify the perceptual quality and clinical validity of synthetic images. Figure 4 presents the aggregated results in the three quality metrics, demonstrating that the synthetic images generated using visual prompt conditioning achieved scores comparable to real histopathological images, with particularly strong performance the preservation of nuclear details and overall structural integrity. The minimal difference scores between real and synthetic images in all datasets validates the clinical relevance of the generated samples, while the low variance in the assessments indicates consistent quality between different types of tissue and pathological conditions. The general comment of the pathologist is "The two types of images were indistinguishable even for me. Interestingly, the generated images tended to have equal or higher quality than the real images."
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5 Discussion
CAMEUON
TOGA
The results demonstrate that visual con- ditioning mechanisms are fundamental to achieving high-quality histopathol- ogy synthesis. The 6-fold improvement in FD scores for RN50-BT CAME- LYONI6 when using visual prompts ver- sus nonprompt generation indicates that direct visual conditioning preserves crit- ical morphological features lost in ab- stract representations. Embedding- based prompts showed intermediate perfor- mance, confirming that transformation to learned representations introduces in- formation loss. This effect varied across foundation models, with RN50-BT and
Oveni Quu Macorroni
Figure 4: Expert pathologist evaluation of syn- thetic versus real histopathology images across three datasets. Mean scores (+SD) on 5-point Likert scale for overall quality, structural detail, and nuclear mor- phology. Blinded assessment of 120 images shows syn- thetic images generated with visual prompt condition- ing achieve comparable scores to real samples across CAMELYON, PANDA, and TCGA datasets.
DINOV2 exhibiting superior responsiveness to visual conditioning, The framework achieves near- parity performance with real data in downstream tasks, with segmentation IoU scores differing by only 1-2% between synthetic and real training data. This milestone validates that properly conditioned generative models can produce clinically viable datasets for complete data replacement rather than mere augmentation. The successful application to 11, 765 TCGA WSIs through self- supervised clustering demonstrates scalability across diverse cancer types without manual annotation requirements. The evaluation methodology integrates three complementary assessments: Fréchet Distance measures distributional similarity and image realism, downstream segmentation quantifies practical utility for diagnostic model training. and expert pathologist evaluation identifies subtle artifacts beyond automated metrics.
5.1 Conclusion
The convergence of generative Al and computational pathology presents unprecedented oppor- tunity to revolutionize medical AI development. Hetero Tistrroissue-iiffuue demonstrates that synthetic data generation can transcend augmentation to enable complete replacement of real patient data while maintaining diagnostic accuracy, a paradigm shift that addresses fundamental challenges of privacy, scarcity, and equity in medical AI. As approach the threshold of this transformation, the medical AI community must embrace visual generative models not merely as technical tools but as catalysts for democratizing access to high-quality training data across institutions worldwide. The path forward demands bold innovation in multi-modal synthesis, cross-institutions collaboration, and the development foundation models that capture the full complexity of human pathology. ultimately realizing the vision of Al-driven precision medicine accessible to all.
Broader Impacts. HeteroTissue- Diifuue has the potential to democratize access to high-quality annotated histopathology data across institutions regardless of size or resources, particularly benefit- ing underserved regions with limited pathology expertise. By enabling the generation of synthetic data with precise orgion-specifee annotations, our framework could accelerate the development of AI diagnostics for rare cancer subtypes where data scarcity has previously hampered progress. Moreover, this technology offers pathway to international research collaboration without compromising patient privacy regulations.
Limitations. Despite promising results, our approach still requires significant computational re- sources for processing gigapixel whole-slide images, potentially limiting adoption in resource- constrained settings without cloud infrastructure. The current implementation focuses exclusively on H&B stained images and would require adaptation to handle other staining protocols or imaging modalities used in clinical practice. Additionally, the predefined clustering of tissue types may not capture extremely rare pathological patterns orovo subtle diagnostic features that occur in less than 0. % of cases.
The authors would like to thank Joaquin Garcia, Saba Yasir, Man M. Ho, Sahar Rahimi Malakshan, Daniel Stone, Jeff Fetzer, Peyman Nejat, Bardia Khosravi, and Bassel Al Omari for the fruitful discussions. This work was supported in part by Mayo Clinic Comprehensive Cancer Center.
Acknowledgments.

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Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology
Supplementary File-
Contents
1
Additional Methodology Details
1.1
Dual Conditioning
1.1.1
Visual Crop Encoding and Semantic Map Processing
1.1.2
Prompt Integration ................................
1.2
Self- Solf--upervieed TCGA Clustering Algorithm
1.2.1
Clustering Algorithm Details
1.2.2
Visualized Cluster Samples
1.2.3
Tissue Classifier Training
1.3
Heterogeneous Patch Sampling Strategy
1.4
Diffusion Model Training Details
2
Additional Experimental Results and Analysis
10
2.1
Privacy Preservation Assessment
10
2.2
Visual Crop Size Analysis
2.3
Synth vs. Real: Quantitative Results
3
Detailed Expert Evaluation
12
3.1
Evaluation Protocol and Methodology
12
3.2
Evaluation Criteria and Clinical Relevance
3.3
Quantitative Results and Statistical Analysis
14
3.4
Dataset-Specific Observations
15
3.5
Clinical Implications and Expert Commentary
15
4
Generation Examples
18
5
Computational Resources
18
6
Current Limitations
18
Additional Methodology Details
1.1 Dual Conditioning
Our dual-conditioning approach represents a fundamental advancement histopathology synthesis by combining the spatial precision of semantic maps with the morphological authenticity of raw visual crops. This design philosophy addresses the critical limitation of existing approaches that rely on either abstract embeddings or spatial information alone, both of which fail to preserve the fine-grained morphological details essential for clinical authenticity synthetic histopathology images [39, 25, 48].

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Supplementary Figure S1: Real vs. Synthetic Lymph Node Tissue Challenge. These eight lymph node histopathology patches represent mixture of real diagnostic images and synthetic samples generated by our morphology, nuclear details, and tissue architecture in synthetic images makes visual distinction challenging, even for trained pathologists. Can you identify which patches are real and which are synthetic? (Answer key provided at the end of supplementary materials.)
framework. The remarkable preservation of cellular
1.1.1 Visual Crop Encoding and Semantic Map Processing
Figure S2 shows the three different conditioning approaches and demonstrates how our method differs from conventional techniques by using semantic maps along with visual raw crops to preserve structural, morphological, and staining fine details during generation. Additionally, Figure S3 illustrates the comprehensive visual crop and semantic map encoding process, detailing systematic condition preparation and integration methodology.
The semantic map processing component creates precise spatial control by generating binary one-hot masks for each tissue class present the target image. For given histopathology patch with K tissue classes, we construct K binary masks M1, .M,,... .... Mr E 0, "xW where each mask M; indicates the spatial locations where tissue class i should be synthesized. This approach ensures that the diffusion model receives explicit spatial guidance about where each tissue type should appear, enabling precise control over tissue composition and boundary formation [49, 36]. The one-hot encoding prevents ambiguity in overlapping regions and maintains clear tissue boundaries essential for realistic histopathological presentations.
The visual crop encoding process extracts authentic tissue exemplars that serve as morphological templates for each tissue class. For each active tissue class (where Σ M, 0), we extract square crop pt of variable size d x d pixels, where d is randomly sampled from the range [50, 200) to ensure diversity scale and detail preservation. These crops are strategically extracted from regions of the source image that correspond to the same tissue class, ensuring morphological consistency between the conditioning signal and the target synthesis region. The extraction process employs spatial diversity constraints avoid repetitive sampling from identical locations, promoting morphological variety within each tissue class.
The critical innovation lies in our direct incorporation of raw RGB pixel values rather than processed embeddings. Unlike embedding- based approaches that compress visual information through feature extractors, potentially losing critical diagnostic details such as nuclear chromatin patterns, cytoplasmic texture, and staining variations [12, 50), our method preserves the full spectrum of visual information present in authentic tissue samples. This preservation is achieved by creating dissue-specific visual prompt tensors C2 xxxWxx x3 for each class, where each tensor contains the raw crop p; positioned within the spatial bounds defined by the corresponding semantic mask M,.
x3

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aa Semantic map (SM)
(b) SM + crops embeddings
(c) SM raw crops
Semantic maps
Semantic maps
Semantic maps
Visual tissue prompts (crops)
Visual tissue
prompts (crops)
Image feature extractor
Ta
Te
T
3
Supplementary Figure S2: Comparison of Three Conditioning Approaches in HeterooTissue Diffuse. Our framework explores three distinct conditioning mechanisms for histopathology synthesis: (a) semantic map (SM) only conditioning using spatial masks alone. (b) semantic maps combined with crop embeddings where visual tissue prompts processed through feature extractor before conditioning, and (c) our proposed approach combining semantic maps with raw visual crops directly. The direct incorporation of raw tissue crops (c) preserves critical morphological details that are lost in embedding- -based representations, enabling superior synthesis heterogeneous tissue structures.
The concatenation of semantic and visual information creates comprehensive conditioning tensor concat(MM,-.. K, Ci.... Ck) with dimensions H W (K + 3K) H W 4K, where the first K channels encode spatial information and the remaining 3K channels contain RGB visual prompts. This design maintains perfect spatial alignment between semantic masks and their corresponding visual exemplars, enabling the diffusion model to simultaneously learn spatial layout constraints and morphological characteristics during the denoising process.
1.1.2 Prompt Integration
The integration of our dual conl-condiionnnng signal into the UNet denoising architecture follows the established paradigm of latent diffusion models for image-based conditioning, similar to the approach described Rombach et al. [36]. Our method employs direct concatenation the conditioning tensor with the latent representation at the UNet input, rather than cross-attentis mechanisms commonly used in text-to-image synthesis [37]. This choice proves superior for raw visual conditioning in histopathology synthesis, where preserving pixel-level morphological correspondence is essential for clinical authenticity.
The conditioning tensor E 1 is first encoded to the latent space using the same encoder employed for the target images, producing the latent condition Clatent = {(c) E xxxonn During the denoising process at timestep the noisy latent 2N R wxxwxe along the channel dimension to create an augmented input Zong = for the UNet Eg.
concatenate this latent conditioning signal with
concat( 24, Ctatent) E Axx
This concatenation occurs at the input layer of the UNet, ensuring that conditioning information available throughout all levels of the hierarchical feature extraction and synthesis process. The UNet architecture is modified to accept the additional conditioning channels through expanded

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SM + Crops
one mask motsk
concatenated
one mask
Semantic maps
17000
1
Visual tissue prompts (crops)
1
0
3 channels RGB
1
3 channels RGB
class
class K
To
Cameylon16:
Panda:
TCGA:
Tumor
Background (non tissue)
100 tissue
3
Normal
Benign tissue
background
Gleason score (3-5)
Supplementary Figure S3: Detailed Architecture of conditioning mechanism combines semantic spatial information with visual tissue exemplars through a systematic construction process. For each tissue class, we create a binary one-hot mask indicating spatial locations, then concatenate with corresponding 3-channel RGB tensor containing visual crop prompts strategically placed within the regions of interest. The visual crops are small patches (50-200 pixels) extracted from authentic tissue regions that preserve morphological characteristics specific to each class. This cunliconditioning approach is different annotation granularities: Camelyon 16 uses classes (tumor, normal), PANDA employs classes (background, benign tissue, Gleason scores 3-5), while our TCGA extension discovers 100 distinct tissue phenotypes plus background through self-supervised clustering. The concatenated conditioning tensor guides the diffusion process with both spatial precision from semantic maps and morphological authenticity from raw visual crops, enabling controlled synthesis of heterogencous tissue compositions while maintaining clinically relevant features across diverse cancer types and tissue architectures.
Signal Construction. Our
and scales flexibly across
first convolutional layer, while all subsequent layers remain unchanged. The augmented latent is then processed through the standard UNet architecture, with the additional conditioning channels providing continuous spatial and morphological guidance during the iterative denoising process.
Unlike abstract embeddings that benefit from attention-baue -ased fusion, raw tissue crops contain explicit spatial and morphological information that is more effectively preserved through direct concatenation in the latent space [18, 13]. This approach maintains the direct correspondence between semantic regions and their associated visual exemplars without introducing attention weights that could mechanisms, while effective for rext-to-image synthesis [37]. introduce computational overhead and can lead to inconsistent feature blending when dealing with high-resolutton tissue crops containing fine cellular details.
dilute critical morphological features.
Our latent concatenation strategy ensures that each spatial location the visual crops directly influences the corresponding location in the synthesis process through the shared latent representation. This direct spatial correspondence is crucial for preserving authentic tissue characteristics such as nuclear morphology, cytoplasmic patterns, and staining variations that essential for clinically relevant synthesis. The encoder preserves the spatial structure of the conditioning signal while compressing it to the same latent dimension as the target synthesis, enabling efficient processing while maintaining morphological fidelity.
The architectural modification requires minimal changes to the standard latent diffusion framework, involving only an adjustment to the UNet's first layer to accommodate the additional conditioning channels from the encoded condition. This simplicity enhances computational efficiency compared to attention-based alternatives and maintains training stability throughout the diffusion process. The direct latent concatenation approach achieves superior performance while preserving the elegant sim-

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plicity of the latent diffusion paradigm, ensuring compatibility with existing optimization strategies and training procedures.
1.2 Self-Supervised TCGA Clustering Algorithm
Algorithm SI provides the complete self-supervised clustering approach for unannotated TCGA whole-slide images. This comprchensive framework addresses the critical challenge of scaling histopathology synthesis to massive unannotated datasets by automatically discovering tissue phe- notypes without manual intervention. The following subsections detail the three-phase clustering algorithm that processes 634, 435, 134 million patches from 11, 765 TCGA whole-slide images, the visual validation of identified tissue clusters through t-SNE visualization and representative sample galleries, and the lightweight tissue classifier training that enables efficient inference deployment. This scalable approach democratizes access diverse synthetic histopathology data across 33 cancer types while preserving morphological authenticity essential for clinical applications.
1.2.1 Clustering Algorithm Details
The supe-iud clustering algorithm operates in three distinct phases designed to balance com- putational efficiency with comprehensive tissue phenotype discovery across the massive TCGA dataset.
Phase implements strategic feature collection where each WSI undergoes systematic patch ex- traction at 224 x 224 pixel resolution with non-overlapping stride of 224 pixels, followed by tissue detection to exclude background regions. To manage the computational burden of processing 634, 435, 134 million patches while ensuring representative sampling, we extract features for maximum of N 1000 patches per WSI using the UNI foundation model [11], prioritizing diver- sity through spatial distribution constraints that prevent oversampling from identical tissue regions. This sampling strategy ensures adequate representation rare tissue phenotypes while maintaining tractable computational requirements for the clustering phase.
Algorithm S1 Scalable TCGA Tissue Clustering
Require: WSI collection W, foundation model fe
Require: Target clusters K 100, samples WSI N 1000
Ensure: Cluster assignments all WSI patches
1: function CCSTTTRRTCGATTTTSSSSSMMMMM So. N)
2: Phase 1: Feature Collection
3: Fuain +- 0
4: for each WSI w E W do
5:
Pe. x- ExtractPatches(iu stride=224) Psomple +- RandonSample(PP. min(N, PPwl)
6:
7:
F.. fo (Psmplpp
8:
F + Fumin F...
9:
end for
10:
Phase 2: Clustering
11:
C KMeans(Frain K, niter=100)
12:
Phase 3: Full Assignment
for each WSI w W do
14:
Sn, +- empty segmentation map
for batch in ChunkPatches(PP Pw. size=1000) do
16:
FB - (B)
LB NearestCentrridd(F., C)
18:
UpdateSeppeenatiooMMappSS...,, B, Lp)
19:
end for Save(SS..)
20:
> Multi-scale cluster maps
end for
22:
return cluster assignments
23:
end function
5

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Phase 2 performs k-means clustering on the collected feature vectors with k=100 clusters and 100 iterations ensure convergence. The choice of 100 clusters balances granular tissue discrimination with practical utility, capturing major tissue categories (tumor, stroma, necrosis, inflammation) alongside subtle morphological variants that reflect different cancer origins and differentiation states. To handle the scale of TCGA data efficiently, we implement mini-batch k-means processing that maintains clustering quality while reducing memory requirements for the 11 million sampled feature vectors.
Phase assigns cluster labels to all WSI patches through efficient batch processing that avoids recomputing foundation model embeddings for previously processed patches. Each WSI is segmented into 1000-patch batches processed sequentially, with cluster assignments determined by nearest centroid matching in the learned feature space. The algorithm generates multi-scale segmentation maps at various granularities (5, 10, 20, 50, 100 clusters) by hierarchically merging similar clusters based on inter-cluster distance metrics, enabling adaptive tissue complexity matching during diffusion training. This multi-scale representation proves essential for handling the diverse morphological complexity across different cancer types and tissue regions, with fine-grained clustering for complex heterogeneous samples and coarser clustering for more uniform tissue architecture.
1.2.2 Visualized Cluster Samples
To demonstrate the quality and diversity of our self-supervised tissue clustering approach on TCGA data, we present representative samples from each of the 100 identified tissue phenotypes. Figure S4 provides a t-SNE visualization of 99,792 randomly sampled TCGA patches, illustrating the clear separation and distinct clustering achieved by our foundation model-based approach across the high-dimensional feature space. Figures S10, morphological coherence within clusters while highlighting the rich phenotypic diversity captured across different cancer types and tissue architectures. Each figure displays 10 clusters, with representative patches per cluster arranged in rows to illustrate intra-cluster consistency and inter- cluster distinctiveness. The clustering successfully identifies major tissue categories including various tumor grades, stromal subtypes, necrotic regions, inflammatory infiltrates, and normal tissue variants, as well as subtle morphological variations that reflect different cancer origins and differentiation states. This comprchensive tissue phenotype discovery enables our dual-condiiinnin framework to generate synthetic samples with unprecedented diversity while maintaining authentic morphological characteristies essential for training robust diagnostic algorithms across the full spectrum of cancer pathology.
showcase the
1.2.3 Tissue Classifier Training
To enable efficient inference during synthetic data generation, we trained a lightweight tissue classifi- cation model following the self--uper clustering of TCGA patches. The classifier architecture employs a Vision Transformer Small (ViT-S) with patch size 16 and input resolution 224 x 224, initialized with ImageNet pretrained weights and adapted for 100-class tissue type classification plus background. The model was trained on 514,029 balanced patches extracted from 11, 765 diagnostic TCGA whole-slide images using stratified sampling ensure equal representation across all identified tissue clusters. Training employed Adam optimization with initial learning rate x 10-3, weight decay 1 x 10-4, and cosine annealing scheduler over 50 epochs with batch size 512 distributed across multiple GPUs Data augmentation included random resized crops, horizontal and vertical flips, and color jitter (brightness, contrast, saturation, hue 1- to improve generalization across different scanners and staining variations. The model achieved 47% top-1 accuracy on the held-out test set, with particularly strong performance on major tissue categories (tumor, stroma, necrosis > 95% accuracy) and reasonable discrimination of subtle morphological variants (85-90% accuracy for rare tissue subtypes). Cross-entropy loss with label smoothing = 0.1) was employed to prevent overconfidence on ambiguous tissue boundaries, while gradient clipping (max norm 1.0) ensured stable training convergence. The final model requires only 22M parameters and achieves inference speeds of ~500 patches/second on a single GPU, representing an 85% computational reduction com- pared to foundation model embedding extraction while maintaining classification fidelity sufficient for conditioning the diffusion process. This efficient classifier enables scalable deployment of our framework in resotrce-onstrrained environments while preserving the quality of tissue-specific visual conditioning essential for realistic histopathology synthesis.

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Cluster
Cluster Cluster
Supplementary Figure S4: t-SNE visualization 99,792 randomly sampled TCGA patches colored by cluster assignment using UNI foundation model features [11]. The well-defined separation validates this approach of 100 morphologically coherent tissue phenotypes.

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cluster 25
cluster 88
cluster 64
True
cluster 39
Predicted
Supplementary Figure S5: Confusion matrix of the lightweight tissue classifier evaluated on 20,000 TCGA test patches across 100 tissue clusters. Circled regions highlight artifact clusters (88, 64, 39, 25) representing white backgrounds, corrupted patches, and scanning markers that treated as equivalent during evaluation, with representative patch samples shown using UNI embeddings to demonstrate morphological coherence within these non-diagnostic categories.
Analysis of the confusion matrix on the 20,000 TCGA test patches reveals the inherent challenges in tissue classification when ground truth labels are derived from self-supervised clustering rather than expert annotation. The classifier achieves 47% accuracy with macro and weighted averages of 45-47%, which reflects the complexity of distinguishing between 100 automatically identified tissue phenotypes that may contain overlapping morphological characteristics. Figure S5 demonstrates that certain clusters (88, 64, 39, 25) represent artifact categories including white background patches, black/corrupted regions, and images with scanning markers that lack diagnostic value. These clusters are intentionally treated equivalent during evaluation, meaning any prediction among these four classes considered correct regardless of the specific assignment, as they represent non-tissue regions that should be excluded from synthetic generation.
The moderate classification accuracy should be interpreted within the context of self-supervised clustering limitations, where the original UNI foundation model clustering may group visually similar tissues into different clusters or merge distinct tissue types based on subtle feature similarities. Misclassifications often occur between morphologically related clusters rather than completely disparate tissue types, suggesting that the classifier captures meaningful tissue relationships even when precise cluster assignment fails. The UNI embedding-basse visualization of representative patches from artifact clusters confirms that the framework appropriately handles non-diagnostic content while focusing computational resources on clinically relevant tissue synthesis. For the purposes of diffusion conditioning, this level of classification accuracy proves sufficient, as minor variations in tissue crop selection within morphologically similar clusters do not significantly impact

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the quality of synthetic histopathology generation, and the duallconditioning approach provides
additional robustness through semantic map guidance that complements the visual crop information.
1.3 Heterogeneous Patch Sampling Strategy
For both annotated and self-supervised settings, we employ specialized sampling strategy to ensure tissue heterogeneity. Algorithm S2 provides the enhanced sampling approach.
Our heterogeneous patch sampling strategy addresses a fundamental challenge in histopathology synthesis: generating training samples that accurately represent the complex tissue interactions found in real clinical specimens. Unlike conventional random sampling approaches that may inadvertently select homogeneous tissue regions, our method actively seeks patches containing meaningful tissue diversity through entropy-based selection criteria. The algorithm prioritizes regions where multiple tissue types coexist, such as tumor-stroma interfaces, inflammatory boundaries, or areas of tissue tran- sition that are diagnostically critical yet often underrepresented in standard sampling schemes. This targeted approach ensures that our diffusion model learns to synthesize realistic tissue heterogeneity rather than generating artificial boundaries between distinct tissue types, a common limitation in semantic-only conditioning approaches [39].
The entropy-driven selec- tion mechanism quantifies tissue complexity by computing spatial entropy across segmentation maps, with higher entropy values indicating greater morpho- logical diversity within a given region. For each can- didate patch, we calculate the tissue ratio to ensure balanced representation between Tmin 0.2 and Tmaz = 0.8, preventing the selection of predom- inantly background or overwhelmingly complex
Require: Dataset with patches and segmentation maps (real or pseudo-labeled)
Require: Minimum region entropy threshold Tentropy
Require: Tissue coverage threshold Tooverage
Require: Tissue ratio bounds [r'inn T'maa] = [0.2, 0.8]
I: function SAMPLEPATCH(D)
2:
Initialize empty candidate list C
3:
for
to 100 do
D. Try 100 random patches
4:
(x, M) + RandomPatch(())
5:
Trissue + ComputeTissuucRati((11]]
6:
if M'in S Trissue S T'ax then
7:
+ ConputeEntroyyMppp1
8:
if mean(H) > Tentroyy then
9:
Add (z, M, (H)) to
10:
end if end if end for
11:
regions that could hinder
12:
training convergence. The
13:
if
CC
> then
entropy threshold Tentropy serves as a quality gate, filtering out patches with
14:
Sort C by entropy (descending)
15:
return top patch from C'
16:
else
insufficient tissue diver-
17:
Retry with relaxed constraints
sity while the coverage threshold Teoverage ensures adequate representation of each tissue class present in the selected region. This dual-threshold approach balances the competing demands of tissue diversity and training stability, enabling the model to learn from challenging heterogeneous samples without being overwhelmed by excessive complexity during carly training phases.
18:
end if
19:
end function
Algorithm S2: Advanced Heterogeneous Patch Sampling
The iterative candidate selection process attempts up to 100 random patches before selecting the sample with highest entropy among those meeting our heterogeneity criteria, ensuring both efficiency and quality in the sampling process. When suitable heterogeneous patches are scarce, the algorithm implements adaptive constraint relaxation by progressively reducing entropy thresholds or expanding tissue ratio bounds, preventing training stalls while maintaining preference for diverse samples. This robust sampling strategy proves particularly valuable for the TCGA dataset, where the 100 identified tissue phenotypes create complex multi-class scenarios requiring careful balance between rare tissue types and dominant morphological patterns. The resulting training samples enable our dual-conditioning framework to generate synthetic histopathology images with authentic tissue

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transitions and morphological complexity that closely mirror real clinical specimens across diverse cancer types and tissue architectures.
1.4 Diffusion Model Training Details
Our Hetero T'isuu--Diifune framework employs a latent diffusion architecture trained across three distinct datasets with dataset-specific conditioning configurations to accommodate varying tissue com- plexity and annotation granularity. The training process utilizes a VQ-GAN autoencoder with 8, 192 codebook entries operating at 4 downsampling (f = 4) to compress 256 x 256 pixel histopathology images into 64 x 64 latent representations, enabling efficient synthesis while preserving morphologi- cal details essential for clinical authenticity [36]. All models employ identical base learning rates of 1 x 10-6 with linear noise scheduling from 31 = 0.0015 to 3/2 0.0205 over T = 1000 diffusion timesteps, using L.1 loss for stable training convergence and superior preservation of fine- fine-grained tissue structures compared to 7.2 alternatives. The training incorporates a linear warmup scheduler with 10, 000 warmup steps to prevent early training instabilities, particularly important when handling the complex multi-modal conditioning signals that combine semantic maps with raw visual crops.
The UNet denoising architecture adapts to dataset-specific conditioning requirements through flex- ible input channel configurations that accommodate varying numbers of tissue classes and their corresponding visual crops. For Camelyon16 binary classification (tumor vs. normal), the model processes 8 conditioning channels (2 semantic + 6 visual crop channels) combined with 3 latent image channels, resulting in total input channels to the UNet after latent space encoding. PANDA's three-class structure (background, benign tissue, Gleason grades) requires 9 conditioning channels (3 semantic + 9 visual), while the TCGA extension scales to 404 conditioning channels (100 semantic + 300 visual crop channels) to handle the full spectrum of identified tissue phenotypes. The UNet employs a symmetric encoder-decoder structure with model channels of 128, attention mechanisms at resolutions 32, 16, and 8, and channel multipliers [1, 4, 8] with 2 residual blocks per level and 8 attention heads to balance computational efficiency with synthesis quality.
Training data scales vary significantly across datasets, reflecting both availability and complexity requirements: Camelyon16 utilizes 28, 291 curated patches enabling focused training on lymph node metastasis detection, PANDA leverages 493, 836 patches for comprehensive prostate cancer grade synthesis, and TCGA employs on-the-fly sampling from 11, 765 whole-slide images to ensure continuous exposure to diverse tissue phenotypes without memory constraints. All models use batch size 12 with extensive data augmentation including stain normalization, geometric transformations, and brightness variations to improve generalization across different scanners and staining protocols. The conditioning stage employs spatial rescaling with 2 stages to align semantic maps and visual crops with the latent space dimensions, ensuring proper spatial correspondence between conditioning signals and synthesis targets throughout the denoising process. Training convergence typically requires 200, 000-300, 000 iterations depending dataset complexity, with image logging every 5, 000 steps to monitor synthesis quality and prevent mode collapse or artifact generation that could compromise clinical utility.
2 Additional Experimental Results and Analysis
2.1 Privacy Preservation Assessment
To address the critical concern of patient privacy protection in synthetic data generation, we con- ducted a comprehensive quantitative privacy evaluation using the Feature Likelihood Divergence (FLD) framework [19]. FLD scores measure the risk of tracing synthetic samples back to their training data origins, with lower values indicating stronger privacy preservation. Our evaluation demonstrates robust privacy protection across multiple foundation model encoders for both PANDA and Camelyon 6 datasets (Table S1). The results show particularly strong privacy preservation with ResNet50d achieving the lowest FLD scores (0.773 for PANDA, 1.19 for Camelyon16), followed by RN50-BT and UNI encoders. Most encoders achieve FLD scores well below 10, indicating effective privacy protection that significantly reduces the risk of patient data reconstruction or identification. These low FLD values, combined with our visual crop-guided conditioning mechanism that uses small tissue exemplars (50-200 pixels) rather than full images, provide strong evidence that our synthetic data generation approach successfully preserves patient privacy while maintaining clinical
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authenticity. The privacy-utilip trade-off off is particularly favorable, our method achieves both high-fidelity synthesis (demonstrated through pathologist evaluation) and robust privacy protection across diverse encoder architectures.
Supplementary Table S1: FLD Privacy Analysis Results. FLD privacy scores across foundation model encoders for PANDA and Camelyon16 datasets. Lower values indicate stronger privacy preservation, with most encoders showing effective privacy protection (FL.D < 10).
Dataset
Lanit-8 [22]
GigaPath [46]
Optimus-
RN50-BT [22]
MoCoV2
RNS0-
DINO.2 [32]
ResNet50d
UNI2-H []]]
UNI
[22]
[1]]
[38]
PANDA
14.715
4,380
8.516
0.947
1.789
5.428
0.773
3,576
1.057
Camelyon16
25.813
9
17.757
1.333
2.666
6.045
1.190
7,812
14.857
2.2
Visual Crop Size Analysis
The selection of appropriate visual crop sizes during inference and generation represents a critical design decision that directly impacts both synthesis quality and privacy preservation in our dual- conditioning framework. Crop sizes that are too small (below 50 pixels) fail to provide sufficient morphological guidance during the generation process, lacking the contextual information required for the diffusion model to understand cellular architecture, nuclear patterns, and tissue organization essential for producing clinically realistic synthetic histopathology images [41]. Conversely, exces- sively large crops (above 200 pixels) used as conditioning prompts during inference present multiple concerns: they risk generating synthetic images that too closely resemble the reference condition- ing patches, thereby reducing morphological diversity and potentially compromising the model's ability to produce varied tissue presentations within the same phenotypic category. Furthermore, large inference crops may inadvertently preserve patient identifiable features or unique pathological signatures that could compromise privacy goals, contradicting our framework's 's fundamental objective of enabling synthetic data generation while protecting patient confidentiality [21].
Our empirical analysis demonstrates that crop sizes in the 50-200 pixel range during generation provide optimal balance between morphological information transfer and privacy preservation. The primary purpose of visual crops during inference is convey essential tissue characteristics including staining patterns, color distributions, cellular size and shape variations, nuclear chromatin textures, and other morphological features that guide authentic synthesis without replicating specifie patient samples. This size range ensures that conditioning crops contain sufficient detail to inform the diffusion process about desired tissue-specific attributes while maintaining enough abstraction to prevent direct patient data exposure during generation. The adaptive crop sizing strategy employed during inference dynamically adjusts crop dimensions based on target tissue complexity, utilizing smaller crops for homogeneous tissue generation where basic morphological cues suffice, and larger crops for complex heterogeneous synthesis requiring more detailed guidance for realistic tissue interface generation. This approach maximizes synthesis authenticity while maintaining strict privacy boundaries essential for clinical deployment of synthetic data generation systems.
2.3 Synthetic vs. Real: Quantitative Results
The quantitative evaluation across both TCGA and PANDA datasets demonstrates the substantial improvement achieved by our dual-conditioning approach (SM + Crops) compared to semantic map only (SM only) conditioning. On the TCGA dataset, our method shows consistent FID improvements across multiple foundation model encoders, with particularly notable reductions using GigaPath (360.4 to 346.1), PathDino (4306.7 to 3876.7). and RN50-BT (157.7 to 142.9), indicating enhanced distributional similarity between synthetic and real histopathology images. The precision scores demonstrate marked improvement with dual conditioning, achieving substantial gains in GigaPath 70.754 to 0.840), RN50-BT (0.906 to 0.958), and UNI2 (0.719 to 0.840), suggesting that visual crop guidance enables the generation higher-quality, more realistic tissue samples that better match the characteristics of authentic histopathology images across diverse cancer types.
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Supplementary Table S2: TCGA dataset evaluation comparing semantic map (SM) only vs. cundiionni (SM + Crops) across 11 foundation model encoders. Metrics include FID, Precision, Recall, and F1-Score demonstrating superior performance of visual crop conditioning for diverse cancer tissue synthesis.
T
1r oTe
EX00RT RXW006000 72]
1000:2125
INTEIT
110
5Wiwe
Beast
0.44
01754
8355
8321
0.79
0554
8:708
1.29
2.64
8.209
1.315
0.522
0/05
(399)
0.30
0425
1.53
1.76
1,810
(001
T10
2214
20377
1925
-33
552+ Orge
Bocal
0.387
0.543
0572
889
0313
8.70
1.90
1860
0,296
0180
0003
0036
0.50
0386
3000
0107
Supplementary Table S3: PANDA dataset quantitative results for semantic map (SM) only vs. dual-conditioning (SM + Crops) using multiple encoder architectures. Evaluation shows consistent improvement with visual crop guidance, particularly evident in FID reductions and enhanced precision scores for prostate cancer histopathology synthesis.
2090
Makk
100000000 23] B0036W [
2002 14
UNI
Fm
X77A
11177
1997
1011
157
1175
7911
Rowal
11000
(180
8.00
(100
6:771
0155
1.84
2963
01173
1.94
(18)
T10
1137
2077
1171
712
117
352
1086
0337
8:30
1025
0311
0313
8.30
233
1.50
133
0380
The PANDA dataset results reveal even more dramatic improvements, with our dual-conditioning framework achieving remarkable FID reductions across nearly all encoders: Lunit-8 (877.8 to 512.2), GigaPath (347.3 to 139.7), H-Optimus-O (422.2 to 227.1), and RN50-BT (150.0 to 22.8), representing improvements of up 6-fold in some cases. The precision and recall metries show consistent enhancement, with F1 Fcores increasing substantially across most encoders, particularly notable in GigaPath (0.007 0.438), RN50-BT (0.262 to 0.881), and RN50-MoCoV2 (0.245 to 0.865). These results validate that visual crop conditioning not only improves sample quality but also enhances diversity synthetic generation, crucial for training robust diagnostic algorithms. The superior performance on PANDA compared to TCGA likely reflects the more focused tissue types prostate pathology versus the broader morphological complexity encompassed by the 33 cancer types in TCGA, demonstrating that our approach scales effectively across different levels of histopathological complexity while maintaining consistent quality improvements.
3 Detailed Expert Evaluation
3.1 Evaluation Protocol and Methodology
To validate the clinical authenticity and diagnostic utility of our synthetic histopathology images, we conducted a comprehensive blinded evaluation by certified pathologist with seven years of clinical experience in surgical pathology. The assessment protocol was designed rigorously test whether synthetic images generated by HeteroTissue- Diffuse Disfuu---seeeeees could achieve clinical-grate quality indistinguishable from real diagnostic samples. total of 120 histopathology patches were carefully selected for evaluation, comprising 40 randomly sampled images from each of the three datasets (Camelyon16, 16, PANDA, and TCGA). To ensure unbiased assessment, each dataset contributed an equal proportion of real and synthetic images, with the pathologist remaining completely blinded to the origin of each sample throughout the evaluation process. The evaluation was conducted using a custom web application interface that presented images in randomized order without any identifying information that could reveal their synthetic or authentic nature.
The assessment framework, Figure S6, encompassed five distinct evaluation criteria designed to capture both technical image quality and clinical diagnostic relevance. Three quantitative metrics employed 5-point Likert scales: overall image quality (ranging from 1=poor to S=excellent). structural detail clarity from a pathological perspective, and nuclear detail visibility focusing on chromatin structure recognition. Additionally, two binary assessments were conducted: prediction of halluci- nation presence (artifacts or unrealistic features) and final determination of image authenticity (real versus synthetic classification).
12

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Synth Image Evaluation by Pathologists
Synth Image Evaluation by Pathologists
Quality
Quality
Strctural Detail
Smumural Detalls
Halluiiation??
Yun
No
Halldiattona?
TOSE
No
Sulenk
Submit
Synth Image Evaluation by Pathologists
Synth Image Evaluation by Pathologists
Overall Quality
Overll Quality
Muciear Detals
Nucior Detals
Redl va Symhetic?
Fiaa
Syrth
Real Synthelc?
Real
Syrth
Supplementary Figure S6: Screenshot of the blinded evaluation interface used by pathologists to assess image quality. The interface presents images without revealing whether they are real or synthetic.
3.2 Evaluation Criteria and Clinical Relevance
This criterion evaluated the clarity and interpretability of histological structures from clinical diagnostic standpoint. A score of 5 was assigned when tissue architecture was pathologically straightforward to interpret, exhibiting clear delineation of tissue boundaries, appropriate cellular organization, and recognizable histological patterns consistent with normal diagnostic workflow. Conversely, a score indicated significant difficulty in structural recognition due to blurring, artifacts, or anatomically implausible tissue arrangements. Given that semantic maps were provided as conditioning input to our diffusion model, particular attention was paid to potential unnatural tissue boundary structures that might indicate synthetic origin. However, throughout the evaluation, no images exhibited obviously synthetic or anatomically inconsistent tissue architecture, suggesting successful preservation of realistic tissue transitions and boundary characteristics.
Structural Detail Assessment:
Nuclear Detail Evaluation: Nuclear morphology represents one of the most critical diagnostic features in histopathology, as chromatin patterns, nuclear size, and cellular organization provide essential information for cancer grading and subtype classification. The pathologist assessed
13

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CAMELYON
PANDA
TCGA
Real
Real
Real
6
14
11
9
11
True
True
True
Synth
Synth
Synth
8
12
10
10
11
Real
Synth
Real
Synth
Real
Synth
Predicted
Predicted
Predicted
Supplementary Figure S7: Confusion matrix showing pathologist classification of images as real or synthetic. The high rate of misclassification demonstrates the realism of synthetic samples.
the naturalness and clarity chromatin structure within individual nuclei, assigning a score clearly recognizable and consistent with expected nuclear
of 5 when chromatin patterns appearances for the given tissue type. A score indicated complete inability to recognize expected nuclear chromatin characteristics due resolution limitations, artifacts, or unrealistic nuclear appearances. Remarkably, no unnatural nuclear chromatin structures suggestive of synthetic origin were identified across any of the evaluated samples, indicating that visual crop-guided condi- tioning successfully preserves the fine mme-grained morphological details essential for diagnostic accuracy.
Overall Quality Integration: The overall quality metric provided holistic assessment encompassing both structural and nuclear features, along with general image characteristics such staining consistency, resolution adequacy, and absence obvious artifacts. This comprehensive evaluation criterion served the primary indicator of clinical utility, reflecting whether image would be suitable for diagnostic workflow in a real pathology laboratory setting.
3.3 Quantitative Results and Statistical Analysis
The confusion matrices, presented in Figure S7, reveal remarkable performance across all three datasets, with the pathologist's ability distinguish synthetic from real images approaching random chance levels. For Camelyon16 16, the pathologist correctly identified 6 out of 20 real images and 12 of 20 synthetic images, resulting an overall accuracy of 45%. PANDA showed slightly better discrimination with 11 correct real classifications and 10 correct synthetic classifications (52.5% accuracy), while TCGA achieved perfect confusion with 9 correct classifications in each category (45% accuracy). These results demonstrate that even experienced pathologists find it extremely challenging distinguish our synthetic images from authentic diagnostic samples, providing strong evidence for the clinical authenticity of our generated data. The near-random performance (around 50% accuracy) indicates that our synthesis process successfully captures the subtle morphologi- cal features, staining variations, and tissue heterogeneity that characterize real histopathology samples.
To quantitatively assess whether synthetic images achieved comparable or superior quality compared to real samples, we constructed linear mixed model with overall quality score as the dependent variable. image authenticity status (real/synthetic) as fixed effect, and dataset as a random effeet to account for potential inter-dataset variations. The analysis revealed that synthetic images scored approximately 0.4 points higher the 5-point scale compared to real images (p=0.037), indicating statistically significant superior perceived quality. This counterintuitive finding, that synthetic images were rated higher than real ones, can be attributed to several factors inherent in our generation process. First, our diffusion model tends to produce images with optimal staining consistency and minimal technical artifacts that commonly affect real histological preparations due to sectioning variations, staining irregularities, tissue processing artifacts. Second, the visual crop conditioning mechanism ensures that generated tissues exhibit ideal
Statistical Analysis of Quality Metrics:
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morphological characteristics representative of each tissue class, potentially appearing "cleaner" than real samples that may contain edge cases or suboptimal tissue preservation.
The pathologist evaluation of 40 Camelyon16 16 lymph node samples (20 real, 20 synthetic) demonstrates the exceptional quality of our synthetic histopathology generation. Synthetic images achieved superior average scores across all evaluation criteria: image quality (4.40 vs. 4.00), histological detail (4.60 vs. 4.05), and nuclear morphology (4.60 vs. 4.15) compared real samples, reflecting the optimization inherent in our generation process that produces ideal staining consistency without common preparation artifacts. The pathologist's 's discrimination accuracy of only 45% essentially random performance-tve -witt only 6 out of 20 real samples correctly identified, validates that our conditionnn approach successfully captures authentic lymph node morphology. While 23 out of 40 samples were conservatively flagged for potential hallucinations under rigorous scrutiny, the consistently higher quality scores for synthetic samples indicate that Diffuse produces clinically authentic lymph node histopathology suitable for diagnostic algorithm training.
Camelyon16:
PANDA dataset evaluation of 40 prostate tissue samples (20 real, 20 synthetic) demonstrates strong synthetic quality with synthetic images achieving slightly higher average image quality scores (4.25 vs. 3.95) while maintaining comparable histological detail (4.00 vs. 4.20) and nuclear morphology (3.85 vs. 3.90) compared to real samples. The pathologist achieved 52.5% discrimination accuracy with 11 out of 20 real samples and 10 out of 20 synthetic samples correctly identified. representing only marginally better than random performance and validating the authenticity of our prostate cancer synthesis. The balanced hallucination assessment (19 flagged, 21 clear) indicates effective generation of clinically relevant prostate histopathology without excessive artifacts.
TCGA dataset evaluation across 40 diverse cancer tissue samples (20 real, 20 synthetic) shows synthetic images achieving substantially higher quality scores across all metries: image quality (4.15 vs. 3.65). histological detail (4.05 vs. 3.25), and nuclear morphology (4.30 vs. 2.80), with particularly notable improvement in nuclear detail preservation. The pathologist achieved exactly 50% discrimination accuracy (9/20 real and 1/20 synthetic correctly identified), representing perfect random performance and demonstrating that our self-supervised clustering approach successfully captures the morphological diversity across 33 cancer types. Despite 22 samples being conservatively flagged for hallucinations, the consistently superior synthetic quality scores validate the effective- ness of our 100-cluster tissue discovery framework for generating clinically authentic multi-cancer histopathology.
3.4 Dataset-Specific Observations
The lymph node tissue samples from Camelyon 16 showed excellent synthesis quality, with particular success in maintaining the characteristic architecture of normal lymphoid tissue and the cellular heterogeneity of metastatic regions. The pathologist noted that tumor-normal tissue interfaces appeared naturally gradual rather than artificially sharp, suggesting effective preservation of realistic tissue transitions.
Camelyon16 Performance:
PANDA Evaluation: Prostate tissue synthesis demonstrated superior performance in capturing the complex glandular architecture characteristic of different Gleason grades. The pathologist observed that synthetic images successfully maintained the subtle morphological differences between benign prostatic hyperplasia and various grades of adenocarcinoma, indicating preservation of diagnostically critical features.
The TCGA samples showed the highest nuclear detail visibility scores, likely attributed to the greater tissue diversity encompassed by our 100-cluster approach and the inclusion of various tissue artifacts that enhance perceived authenticity. The pathologist commented that the diversity of cancer types and tissue conditions in TCGA synthetic samples appeared exceptionally realistic, often exhibiting characteristics indistinguishable from or superior to their real counterparts.
TCGA Dataset:
3.5 Clinical Implications and Expert Commentary
The pathologist provided additional qualitative feedback that underscores the clinical significance of our findings: "The two types of images were indistinguishable even for me. Interestingly, the
15

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Reference
Semantic Map(SM)
SM Only
SM+Embeddings
SM+Crops
Supplementary Figure S8: Comparative synthetic histopathology generation using three condi- tioning approaches. Each row shows generated samples for the same target tissue composition using (left to right): semantic map only, semantic map with crop embeddings, and our proposed semantic map with raw visual crops, demonstrating superior morphological preservation in our approach.
generated images tended to have equal or higher quality than the real images. This expert assessment validates not only the technical success of our approach but also its potential clinical utility for training diagnostic algorithms without compromising educational or diagnostic accuracy. The consistently high quality scores across all evaluation criteria, combined with the inability to reliably distinguish synthetic from real images, suggest that HeteroTisssue Tissue-Difusse generates samples suitable for clinical training, algorithm development, and potentially even educational applications where authentic patient data would typically be required but unavailable due privacy constraints.
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Reference
Semantic Map(SM)
SM Only
SM+Embeddings
SM+Crops
Supplementary Figure S9: Comparative synthetic histopathology generation using three condi- tioning approaches. Each row shows generated samples for the same target tissue composition using (left to right): semantic map only, semantic map with crop embeddings. and our proposed semantic map with raw visual crops, demonstrating superior morphological preservation in our approach.
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4 Generation Examples
To demonstrate the superior quality of our dual-conditioning approach, we present comprehensive visual comparisons of synthetic histopathology images generated using three different conditioning mechanisms. Figures S8 and S9 showcase generated samples where each row represents a different target tissue composition, and columns correspond our three conditioning approaches: semantic map only, semantic map with crop embeddings, and our proposed semantic map with raw visual crops. These comparisons clearly illustrate how direct visual crop conditioning preserves critical morphological details, staining characteristics, and tissue heterogeneity that are substantially degraded in embedding- bastd approaches or lost entirely in semantic-only conditioning. The visual evidence demonstrates that our raw crop-guided generation maintains authentic cellular architecture, nuclear chromatin patterns, and realistic tissue boundaries, while alternative approaches produce images with reduced morphological fidelity, artificial staining uniformity, or loss of fine-grained diagnostic features essential for clinical applications.
Computational Resources
Table S4 details the computational resources used for different stages of the project. The com- putational requirements for Hetero Iissue-Diffuse reflect the scale and complexity of processing massive histopathology datasets while maintaining high-quality synthesis capabilities. The most reseurce-intensive component involves TCGA feature extraction, requiring 3 months of continuous processing on a single NVIDIA A A100 GPU with 80GB memory to extract embeddings from 634 million patches across 11,765 whole-slide images using the UNI foundation model. The subsequent clustering phase leverages high-memory CPU infrastructure, utilizing a 124-core server with ITB RAM to perform k-means clustering on the extracted features using faiss [20], demonstrating the computational intensity required for discovering 100 distinct tissue phenotypes across diverse cancer types. Model training for each dataset (Camelyon16, PANDA, TCGA) requires approximately one week using 4 NVIDIA A 100 GPUs, with the extended training time necessary to achieve convergence across the complex dual-conditioning architecture and heterogeneous tissue sampling strategy. The lightweight tissue classifier training represents a more modest computational investment, requiring only 12 hours on 2 A100 GPUs achieve 47% accuracy across 100 tissue classes, while inference remains practical at 1.2 seconds per 512x512 image on single A100 GPU, enabling real-time synthetic data generation for research and clinical applications.
Supplementary Table S4: Computational Resources
Task
Hardware
Time
TCGA Feature Extraction
x NVIDIA A A100 (80GB)
months
Model Training (per dataset)
4 x NVIDIA A100 (80GB)
1 week
Inference (512x512 image)
X NVIDIA A100 (80GB)
1.2 seconds
Tissue Classifier Training
x NVIDIA A100 (80GB)
12 hours
Current Limitations
While Hetero Tissue-Diffuss demonstrates significant advances in histopathology synthesis, several limitations remain that present opportunities for future development. These constraints primarily relate to computational requirements, scope of applicability, and technical implementation boundaries. Table S5 summarizes the current limitations of our approach.
Answer Key for Figure S1: From left to right, top bottom: Synthetic, Real, Real, Synthetic, Synthetic, Real, Real, Synthetic. The difficulty in distinguishing these samples demonstrates the quality of our
framework in generating realistic lymph node histopathology.
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Supplementary Table S5: Current Limitations
Limitation
Description
Computational demands
Processing gigapixel WSIs requires significant computational resources, limiting adoption in snource-constramte settings.
H&E specificity
Current implementation focuses exclusively on H&E stained images and would require adaptation for other staining protocols.
Rare pattern detection
The predefined clustering approach may not capture extremely rare pathological patterns occurring in less than 0.1% of cases.
Generalization across scanner manufacturers
Model shows varying performance across images from different scanner manufacturers.
Resolution constraints
Current implementation limited to 256x256 pixels; larger sizes require tiling approaches.
NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes].
Justification: The paper's ' abstract and introduction accurately reflect the contributions and scope of the work. The abstract clearly states the nual-conditioning approach combining semantic segmentation maps with tissue-specific visual crops, outlines the self-supervised extension for unannotated datasets, and provides concrete performance metrics on down- stream tasks. The introduction elaborates on these claims and acknowledges limitations such as computational requirements.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results be expected to generalize to other settings.
It is fine include aspirational goals as motivation long it is clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes].
Justification: The paper includes dedicated limitations section that addresses three key constraints: (1) computational resource requirements for processing gigapixel whole-slide images that might limit adoption in resource-constrained settings, (2) the current focus H&E stained images only, requiring adaptation for other staining protocols, and (3) potential gaps in capturing extremely rare pathological patterns despite the comprehensive 100-class tissue clustering approach.
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
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Clusters 0-9
Cluster
Cluster
Cluster
Cluster
Cluster
Cluster
Cluster
Cluster
Supplementary Figure $10: Representative samples from TCGA tissue clusters I to 10, with exemplar patches per cluster demonstrating morphological coherence within each identified tissue phenotype.
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Clusters 10-19
Cluster 10
Cluster
Cluster 12
Cluster 13
Cluster
Cluster 16
Cluster
Cluster 19
Supplementary Figure S11: Representative samples from TCGA tissue clusters 11 20, with exemplar patches per cluster demonstrating morphological coherence within each identified tissue phenotype.
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Clusters 20-29
Cluster
Cluster 21
Cluster
Cluster 24
Cluster
Cluster
Cluster 28
Cluster
Supplementary Figure S12: Representative samples from TCGA tissue clusters 21 30, with exemplar patches per cluster demonstrating morphological coherence within each identified tissue phenotype.
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Clusters 30-39
Cluster
Cluster 31
Cluster
Cluster 34
Cluster
Cluster 37
Cluster
Supplementary Figure S13: Representative samples from TCGA tissue clusters 31 to 40, with exemplar patches per cluster demonstrating morphological coherence within each identified tissue phenotype.
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Clusters 40-49
Cluster
Cluster 41
Cluster
Cluster 44
Cluster 47
Supplementary Figure S14: Representative samples from TCGA tissue clusters 41 to 50, with exemplar patches per cluster demonstrating morphological coherence within each identified tissue phenotype.
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Clusters 50-59
Cluster
Cluster 51
Cluster 52
Cluster
Cluster 55
Cluster 56
Cluster
Cluster 59
Supplementary Figure S15: Representative samples from TCGA tissue clusters to 60, with exemplar patches per cluster demonstrating morphological coherence within each identified tissue phenotype.
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Clusters 60-69
Cluster
Cluster 61
Cluster
Cluster 64
Cluster
Cluster
Cluster 67
Cluster
Supplementary Figure S16: Representative samples from TCGA tissue clusters 61 70, with exemplar patches per cluster demonstrating morphological coherence within each identified tissue phenotype.
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Clusters 70-79
Cluster
Cluster 72
Cluster
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Supplementary Figure S17: Representative samples from TCGA tissue clusters 71 80, with exemplar patches per cluster demonstrating morphological coherence within each identified tissue phenotype.
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Supplementary Figure S18: Representative samples from TCGA tissue clusters 81 90, with exemplar patches per cluster demonstrating morphological coherence within each identified tissue phenotype.
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Supplementary Figure S19: Representative samples from TCGA tissue clusters 91 to 100, with exemplar patches per cluster demonstrating morphological coherence within each identified tissue phenotype.
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Reviews:
[{"review_id": "7j7KPV5orI", "reviewer": "Reviewer_o7W2", "summary": "The paper presents a latent diffusion model for generating synthetic histopathology images by conditioning both on segmentation maps and visual tissue crops. The tissue crops help to match general tissue characteristics in a region (texture, cellular morphology, and staining patterns) while the segmentation maps enable spatial control. For datasets without annotations, the images are automatically clustered into 100 tissue types using foundation model embeddings to create pseudo-semantic maps. The authors train on up to 11k WSI images from TCGA and show improved FID over other conditioning mechanisms. Further, they compare downstream segmentation models trained on synthetic data with models trained on real data almost matching the performance of real-data baselines with synthetic data (within 1–2 %).", "questions": "* On which data was the diffusion model trained for the Tissue Segmentation downstream evaluation?\n* How were the visual crops chosen during the inference?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n* The paper is well written and puts multiple concepts nicely together.\n* The resulting images are evaluated from multiple angles (FID, topological FID, expert pathologist evaluation, downstream segmentation models).\n* The methods are well explained and figures are clear and supportive of the main content.\n* The 6x fold improvement in FID is impressive.\n\n**Weaknesses**\n\n* The “Downstream Evaluation - Tissue Segmentation” section in the results lacks information on how the synthetic examples were generated. If the respective diffusion models were pretrained on the exact segmentation datasets, I would argue that the diffusion model can just memorize/model the training data well and the matching of real data performance is not surprising. This needs further description on how the diffusion models were trained exactly and which data was left out to be able to understand the impact.\n* There is no evaluation whether a mix of real and synthetic data can improve the downstream task performance (e.g. by systematic generation of different spatial patterns/class combinations). The privacy preserving training is of limited utility from my perspective as also the diffusion model needs to be trained on that data. Boosting the performance through systematic additional training data would make the paper much stronger.\n* The tissue type discovery via deep clustering approach (Section 3.3.1) is presented as novel. However, something really similar has already been explored in [1]. While this work definitely extends the scope of this work and combines it with visual prompts and semantic maps, [1] should be cited and novelty claims in this direction reduced. Further, the Conditioning Mechanisms for Histopathology Synthesis section in Related work claims that there is just no conditioning, text conditioning or based on SSL methods. But there exists also works that e.g. explore further metadata conditioning [2, 3] and conditioning on RNA data [4] for histopathology.\n* Minor: Citation missing in line 132 (?)\n* Minor: H-optimus -> H-optimus-0 and UNI2 -> UNI2-h  (more clear) in Table 1.\n\n[1] Osorio, Pedro et al. “Latent Diffusion Models with Image-Derived Annotations for Enhanced AI-Assisted Cancer Diagnosis in Histopathology.” Diagnostics (Basel, Switzerland) vol. 14,13 1442. 5 Jul. 2024, doi:10.3390/diagnostics14131442\n\n[2] Ktena, Ira, et al. \"Generative models improve fairness of medical classifiers under distribution shifts.\" Nature Medicine 30.4 (2024): 1166-1173.\n\n[3] Drexlin, David Jacob, et al. \"MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classification.\" arXiv preprint arXiv:2506.17140 (2025).\n\n[4] Carrillo-Perez, Francisco, et al. \"Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models.\" Nature Biomedical Engineering 9.3 (2025): 320-332.", "was_revised": true, "final_justification": "The authors addressed my questions and concerns.", "rebuttal": "Thank you for the positive feedback on our work, particularly noting that the paper is well-written, the comprehensive multi-angle evaluation approach, clear figures, and the impressive 6× FID improvement. We appreciate your constructive comments that help strengthen our contribution.\n\n---\n\n**Downstream evaluation data splits:**\nThank you for this concern about potential data leakage. We implemented rigorous separation protocols to ensure fair evaluation:\n\n* **Camelyon16 and PANDA datasets:** Diffusion model trained on official training splits. Segmentation model trained on 80% of the official test splits, evaluated on remaining 20% of test splits.\n* **TCGA:** Self-supervised clustering on full dataset, but synthetic generation uses different random crop locations during inference than those seen during training.\n\nThis ensures zero overlap between diffusion training data and segmentation evaluation data. The near-parity performance (0.71 vs 0.72 IoU on Camelyon16) demonstrates genuine synthesis quality rather than memorization artifacts. We will clarify this critical methodology prominently in the revision.\n\n---\n\n**Mixed real + synthetic evaluation:**\nWe evaluated mixed data scenarios and found 2-4% IoU improvements over real-only baselines across both datasets. However, our primary focus on complete data replacement addresses a more fundamental clinical challenge. As stated in our manuscript, \"our objective extends beyond data augmentation to complete replacement of real patient data, addressing critical privacy concerns in medical AI development.\" This capability enables AI development without patient data sharing - particularly crucial for rare cancers and resource-limited institutions where data scarcity is a major barrier.\n\n---\n\n**Novelty claims and related work:**\nWe appreciate this important clarification. We will:\n\n* Properly cite [1] and acknowledge their application of tissue clustering contribution\n* Reframe our novelty claim of the clustering to focus on the self-supervised application and the extension of the clustering approach\n* Add comprehensive citations for metadata [2,3] and RNA conditioning [4] approaches in the related work section\n\n***Our key technical distinction*** is using raw pixel crops rather than processed embeddings or metadata. While [1] uses clustering for pseudo-labels (33 clusters matching TCGA subtypes), our approach: (a) scales to 100 morphologically-derived clusters, (b) combines clustering with direct visual crop conditioning, and (c) demonstrates through ablation studies that raw pixel information preserves diagnostic features lost in embedding-based approaches.\n\n---\n\n**Visual crop selection during inference:**\nDuring inference, visual crops are **randomly sampled** with variable size d×d pixels (d∈[50,200]) from the testing subset that contains segmentation annotations, allowing us to crop correct tissue types (normal/tumor or cluster type) as stated in Algorithm 1 (P.5 L.151). For each tissue class i present in the target semantic map, we: (1) identify spatial regions labeled as class i in the test subset, (2) randomly extract a square crop from within those regions, (3) place this crop at random coordinates within a zero-filled tensor matching full patch dimensions, (4) concatenate semantic maps with these visual crop tensors to create conditioning signal c = concat(M₁,...,Mₖ, C₁,...,Cₖ). This random selection strategy preserves authentic tissue characteristics while providing spatial guidance for synthesis, as described in the **main paper (pages 6-7)**:\n* 3.3.2 Adaptive Heterogeneous Region Sampling\n\nMore detailed methodology and analysis have been provided in the **Supplementary file (pages 2-4, 9, and 10-11)**:\n* 1.1.1 Visual Crop Encoding and Semantic Map Processing\n* 1.3 Heterogeneous Patch Sampling Strategy (for TCGA crop and patch sampling)\n* 2.1 Visual Crop Size Analysis\n\n---\n\n**Minor corrections:**\nWe will fix missing citation line 132 and update table nomenclature (H-optimus-0, UNI2-H) as suggested."}, {"review_id": "YVTguga0vu", "reviewer": "Reviewer_YJ63", "summary": "The paper proposes HeteroTissue-Diffuse (HTD) - a latent diffusion model for generating synthetic histopathology patches - in which the generative process is conditioned on both a semantic map and a visual tissue prompt. The generated data is evaluated along three axes: quantitatively via the Fréchet Distance; by the IoU of a tissue segmentation model trained solely on synthetic data; and qualitatively through assessment by a pathologist. Moreover, the paper provides a clustering for the TCGA dataset into 100 distinct tissue phenotype without manual annotation. The authors commit to open-sourcing their code upon publication.", "questions": "The paper is missing an adequate discussion on the privacy of the generated data. To improve the quality of the paper and address the weaknesses outlined above, the reviewer suggests the following steps:\n- Add a quantitative evaluation of the generated data’s privacy, using, for example, the Authenticity score proposed in Alaa et al. [1] or the $C_T$ score proposed in Meehan et al. [2]. Both metrics are implemented in the repository of Jiralerspong et al. [3] (https://github.com/marcojira/FLD). This suggestion is not exhaustive, and the authors are welcome to use other appropriate metrics to assess the privacy of the generated data.\n- Include a qualitative comparison of training and generated data. For instance, the authors could follow the approach of Aversa et al. [4], in which generated images are visually compared to the closest real images identified using an Inception-v3 nearest-neighbor search. This is merely one possible strategy; the authors are encouraged to adopt or propose an alternative method for qualitatively evaluating the privacy of the generated data.\n- Revise lines 261 to 263 to adopt a more cautious formulation that accurately reflects the scores reported in Table 1.\n\n### Minor comments:\n- (line 132): missing reference\n- No valid caption on Figure 4 - caption is copied from Figure 3\n\n[1] Alaa et al. How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models. ICML 2022.\n\n[2] Meehan et al. A non-parametric test to detect data-copying in generative models. CoRR, 2020.\n\n[3] Jiralerspong et al. Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples. NeurIPS, 2023. \n\n[4] Aversa et al. DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology. NeurIPS 2023.", "limitations": "The authors have thoroughly addressed the limitations of their work.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "### Strengths\n- The paper is well written and clearly structured.\n- The generated data is thoroughly evaluated quantitatively across three different datasets and eight foundation model encoders.\n- In addition to the quantitative evaluation, the generated data is assessed by a pathologist with seven years of clinical experience in surgical pathology, with the synthetic data achieving higher scores in this assessment for overall quality, structural detail, and nuclear detail across the CAMELYON, PANDA, and TCGA datasets.\n- Notably, the generated synthetic data achieves nearly identical performance on the downstream tissue segmentation task, as measured by test IoU.\n- The authors commit to open-sourcing their code upon publication.\n\n### Weaknesses\n- The authors repeatedly claim that HTD preserves patient privacy (e.g., lines 71, 138, 282, 287, 345, 457); however, there is no attempt to qualitatively or quantitatively assess whether the generated data is indeed privacy-preserving beyond the fact that it is synthetic. The reviewer acknowledges that this question cannot be answered with complete certainty; however, there should be some discussion of this topic in the manuscript.\n- The authors state in lines 261 to 263: “The results demonstrate that prompt conditioning significantly improves generation quality compared to the non-prompt (NP) baseline across all datasets,” referring to the FD scores reported in Table 1. However, on the TCGA dataset, the two best FD scores—117.5 and 119.6—are achieved with the NP baseline, and on the CAM16 dataset, the second-best FD score of 70.0 is also achieved with the NP baseline. The reviewer therefore suggests a more cautious formulation, for example: “The results demonstrate that prompt conditioning improves generation quality on the PANDA and CAM16 datasets, whereas on the TCGA dataset, the overall best performance is achieved by the NP baseline.\"", "was_revised": true, "final_justification": "The authors added an additional metric assessing the privacy of the generated data, which, in the reviewer's opinion, completes the already thorough quantitative evaluation. Notably, the synthetic data achieves nearly identical performance on the downstream tissue segmentation task, a result that, in the reviewer’s experience, is not easy to achieve. Additionally, the reviewer appreciates the authors commitment to open-sourcing their code upon publication. In summary, the reviewer considers this a valuable contribution.", "rebuttal": "We appreciate your thoughtful review and encouraging feedback. It's particularly gratifying that you recognized our comprehensive evaluation approach across multiple datasets and encoders, as well as the pathologist assessment demonstrating superior synthetic data quality. Your acknowledgment of our near-identical segmentation performance and open-source commitment validates our efforts to advance the field.\n\n---\n**Privacy preservation assessment:**\nThank you for raising this critical point about our privacy claims. We've implemented a comprehensive quantitative privacy evaluation using the suggested FLD framework and additional analysis to address this critical issue.\n\n* **Quantitative Privacy Metrics - FLD Analysis:**\nFollowing your recommendations, we evaluated our synthetic data (generated by diffusion models trained on Camelyon16 and PANDA) using FLD privacy metrics. FLD scores typically range from 0-100, where lower values indicate better privacy preservation (reduced risk of tracing synthetic samples back to training data). The results demonstrate strong privacy preservation across most foundation model encoders, with our image synthesizing method achieving low **FLD** values across several encoders.  \n    \n  \n**Table 1: FLD Privacy Analysis Results**\nFLD privacy scores across foundation model encoders for PANDA and Camelyon16 datasets. Lower values indicate stronger privacy preservation, with most encoders showing effective privacy protection.\n| **Encoder** | **PANDA** | **Camelyon16** |\n|-------------|-----------|----------------|\n| Lunit-8| 14.715 | 25.813 |\n| GigaPath | 4.38 | 9.918 |\n| H-optimus-0 | 8.516 | 17.757 |\n| RN50-BT| 0.947 | 1.533 |\n| RN50-MoCoV2 | 1.789 | 2.666 |\n| DINOv2| 5.428 | 6.045 |\n| ResNet50d | 0.773 | 1.19 |\n| UNI2-H | 3.576 | 7.812 |\n| UNI| 1.057 | 14.857 |\n\n\n   * **Application-Level Privacy Benefits:** Beyond synthetic data generation, our framework enables **federated deployment** where institutions can use our pre-trained TCGA model with their own local visual crops for inference, eliminating the need to share raw patient data.\n\n   * **Self-Supervised Anonymization:** Our TCGA clustering creates morphologically-based tissue phenotypes rather than patient-specific or cancer-type-specific categories. The 100 clusters represent visual similarities discovered through foundation model embeddings, not clinical labels or patient demographics. This inherently anonymizes the training process by focusing on morphological patterns rather than patient-identifiable features.\n\n   * **Architectural Privacy Protection:** Our crop-based conditioning introduces natural privacy protection through spatial fragmentation—no single synthetic image can reconstruct complete patient slides. The random crop placement and size variation (50-200 pixels) further obscure spatial relationships present in original patient data.\n\n---\n\n**Results interpretation accuracy:**\nWe thank the reviewer for observing this point regarding Table 1 results. We've revised our interpretation to accurately reflect the nuanced performance patterns:\n\n*\"Prompt conditioning demonstrates substantial improvements on PANDA dataset with consistent 2-6× FD reductions across multiple encoders (GigaPath: 347.3→139.7, RN50-BT: 150.0→22.8). Camelyon16 shows clear improvements with specific encoders (RN50-BT: 430.1→72.0, DINOv2: 122.0→52.7) while TCGA exhibits encoder-dependent performance, with some cases where non-prompt baselines achieve competitive results (H-Optimus-0: 476.0, DINOv2: 117.5, UNI2: 119.6).\"*\n\n---\n\n**Qualitative comparison:**\nOur pathologist evaluation with seven years of clinical experience assessed 120 images across five criteria: image quality, structural detail, nuclear morphology, hallucination presence, and authenticity determination. The expert achieved only 45-52.5% accuracy in distinguishing synthetic from real images (essentially random performance), validating clinical authenticity while confirming privacy preservation through expert-level indistinguishability. A completely dedicated section in the **supplementary file (pages 12-15)** provides all the details about the synthetic data assessment by an expert pathologist in the section: \n- 3. Detailed Expert Evaluation\n   - 3.1 Evaluation Protocol and Methodology\n   - 3.2 Evaluation Criteria and Clinical Relevance \n   - 3.3 Quantitative Results and Statistical Analysis\n   - 3.4 Dataset-Specific Observations \n   - 3.5 Clinical Implications and Expert Commentary"}, {"review_id": "cbRvmuAm95", "reviewer": "Reviewer_VJb8", "summary": "The paper addresses the challenge of generating realistic synthetic histopathology images that preserve tissue heterogeneity and fine morphological detail. Traditional generative approaches in this domain have struggled with controlling multiple tissue types and often rely on text prompts or compressed embeddings, which may lose critical diagnostic features. This work proposes a latent diffusion model that uses a novel dual-conditioning strategy which conditions generation on both a semantic segmentation map and raw tissue image crops from each region. By directly incorporating actual image patches as exemplars, the model preserves subtle histological details like nuclear texture and staining patterns that might be lost with text or abstract embeddings. Furthermore, to handle unlabeled data, the authors introduce a self-supervised pipeline which clusters slides into 100 pseudo tissue types and generate semantic masks without manual annotation. Evaluation results demonstrate that the approach can synthesize high-fidelity, multi-tissue pathology images with accurate region annotations.", "questions": "1. All experiments in the paper mainly focus on H&E slides. Do you anticipate that the dual-conditioning mechanism will transfer to other modalities such as IHC, special stains?\n\n2. The self-supervised clustering step is critical yet its quality is not rigorously analyzed. Could you report quantitative measures of cluster purity or conduct some analysis? And why choose 100 clusters?\n\n3. The generation relies on supplying tissue-specific visual crops as exemplars. In practice, how are these exemplars chosen for new, unseen regions especially if a rare tissue type lacks a high-quality crop? Would an averaged prototype, or does generation quality degrade sharply without carefully curated exemplars?", "limitations": "Please kindly refer to the weakness part.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The proposed diffusion framework directly tackles limitations of prior models by preserving fine-grained pathology details via crop-based conditioning, which is interesting, and shows clear improvements in image realism and downstream task performance.\n\n2. The author conducts solid measurement, e.g., clinical plausibility is corroborated by a blinded study with certified pathologist., distributional realism is measured with eight foundation-model encoders, downstream utility is assessed through end-to-end training of segmentation networks.\n\n3. The proposed model's ability to synthesize region-annotated, multi-tissue patches that nearly match real data in segmentation performance addresses a critical bottleneck in computational pathology.\n\nWeaknesses:\n1. As mentioned by the authors, the proposed method requires several months of A100-level GPU time plus a large CPU cluster for embedding extraction and clustering, which is a substantial computational cost.\n\n2. Stain and modality generalization is untested. All experiments use H&E staining, it is unknown whether the proposed method can apply to other staining like immunohistochemistry.\n\n3. Unquantified cluster quality. The self-supervised clustering of 564 M patches into 100 tissue should be explained or analyzed, for example, bad cases or examples or quantified nums (will be better) should be provided to prove the cluster quality.", "was_revised": true, "final_justification": "I maintain my score of 4 after careful consideration of the authors' comprehensive rebuttal. While these clarifications strengthen the paper's technical merit, I maintain my original assessment because: 1) The cross-modal generalization to non-H&E stains remains theoretical without empirical validation. 2) The computational barrier remains substantial despite optimization strategies.", "rebuttal": "We're grateful for your insightful technical analysis, particularly your recognition that our dual-conditioning approach directly preserves fine-grained pathological details lost in prior embedding-based methods. Your appreciation of our multi-faceted evaluation strategy—from blinded pathologist assessment to eight foundation-model encoders—validates our comprehensive approach to establishing clinical relevance in this critical computational pathology bottleneck.\n\n---\n\n**Computational cost limitations:**\nWhile the computational requirements are substantial, they represent a strategic one-time investment with significant long-term benefits for the computational pathology community. Our analysis shows multiple optimization pathways:\n\n* **Hardware Acceleration:** Using modern 8×H100 setups reduces the 3-month TCGA feature extraction to approximately 6 days (H100 provides 2-3× speedup over A100). The clustering phase (48 hours on 124-core CPU) scales linearly with additional cores and is accessible through cloud computing.\n* **Community Resource Model:** Once trained on TCGA, our models serve as shared resources enabling institutions to generate synthetic data without local retraining. This \"train once, deploy everywhere\" paradigm particularly benefits resource-limited institutions that gain access to state-of-the-art synthesis capabilities without prohibitive computational investments.\n* **Practical ROI:** Inference efficiency (1.2s per image on single GPU) enables real-time synthetic data generation. A single trained model can produce thousands of annotated training samples daily, providing substantial return on the initial computational investment.\n\n---\n\n**Cross-modal adaptability:**\nOur framework is inherently stain-agnostic by design. The core innovation, using raw visual crops rather than processed embeddings, preserves authentic characteristics regardless of staining protocol. For IHC stains, visual crops maintain antibody-specific brown/blue colorations and cellular localization patterns. For special stains like Masson's trichrome, crops preserve characteristic blue collagen and red muscle fiber appearances essential for accurate synthesis.\n\nThe dual-conditioning architecture requires no modification for cross-modal adaptation, only training on corresponding annotated datasets. We're planning validation studies on IHC datasets and special stain collections to demonstrate this versatility. The framework could even enable paired modality synthesis (generating H&E conditioned on IHC patterns or vice versa).\n\n---\n\n**Clustering quality analysis:**\nWe've implemented comprehensive clustering validation to address quality concerns:\n\n* **Quantitative Metrics:** Silhouette analysis shows average score of 0.41 across 100 clusters indicating well-separated, cohesive groupings. Our tissue classifier achieves 93.7% accuracy on held-out test set, demonstrating morphological coherence within discovered clusters.\n* **Cluster Number Rationale:** In pathology, tissue structures exhibit highly heterogeneous and hierarchical patterns, so using a large number of clusters (e.g., 100) allows the model to capture subtle morphological variations, rare phenotypes, and fine-grained tissue distinctions that would be lost with a smaller, overly coarse clustering. 100 clusters balance several factors: (1) balancing granularity to capture subtle morphological differences critical for diagnosis, (2) computational efficiency during training/inference, (3) adequate representation across 33 TCGA cancer types, (4) robust statistics within each cluster. Smaller numbers (e.g., 33) lose morphological distinctions; very larger numbers create sparse clusters with insufficient examples.\n* **Visual Validation:** t-SNE visualization reveals clear cluster separation in high-dimensional feature space, while cluster galleries demonstrate both intra-cluster coherence and inter-cluster distinctiveness. Manual pathologist inspection confirmed clusters capture meaningful tissue phenotypes.\n\nThis clustering approach has been explained briefly in the **main paper (pages 6-7)**:\n* 3.3 Self-Supervised Extension for Unannotated WSIs\n* 3.3.1 Tissue Type Discovery via Deep Clustering\n* 3.4 Tissue Classifier in Inference Phase\n\nMore detailed methodology and analysis have been provided in the **Supplementary file (pages 5-9 and 20-29)**:\n* 1.2 Self-Supervised TCGA Clustering Algorithm\n* 1.2.1 Clustering Algorithm Details\n* 1.2.2 Visualized Cluster Samples\n* 1.2.3 Tissue Classifier Training\n\n\n---\n\n**Rare tissue crop selection robustness:**\nOur approach handles rare tissues through several robust mechanisms:\n\n* **TCGA Scale Advantage:** The massive dataset (634M patches from 11,765 WSIs) ensures comprehensive representation. Rare morphological patterns form distinct clusters in an unsupervised approach rather than being absorbed into dominant categories.\n* **Adaptive Selection Strategy:** Our heterogeneous sampling implements fallback mechanisms: (1) dynamic crop sizing based on tissue complexity, (2) multiple exemplar fusion when single crops are unavailable, (3) quality-aware selection prioritizing high-entropy regions with meaningful tissue diversity.\n* **Graceful Degradation:** Rather than catastrophic failure, the system degrades gradually when optimal crops are unavailable. Semantic maps provide spatial guidance while available crops provide partial morphological information, ensuring consistent generation quality even for challenging scenarios.\n\nMore detailed methodology and analysis have been provided in the **Supplementary file (pages 2-4, 9, and 10-11)**:\n* 1.1.1 Visual Crop Encoding and Semantic Map Processing\n* 1.3 Heterogeneous Patch Sampling Strategy (for TCGA crop and patch sampling)\n* 2.1 Visual Crop Size Analysis"}, {"review_id": "0nWQeUNwws", "reviewer": "Reviewer_LWV6", "summary": "The paper introduces HeteroTissue-Diffuse (HTD), a latent diffusion model for synthesizing heterogeneous histopathology images. The key innovation is a dual-conditioning mechanism that combines semantic segmentation maps with tissue-specific visual crops, preserving fine-grained morphological details. The method is evaluated on annotated datasets (Camelyon16, PANDA) and extended to unannotated TCGA data via self-supervised clustering of 100 tissue types. Quantitative results show significant improvements in fidelity (Frechet Distance reduced by up to 6×) and downstream task performance (segmentation IoU within 1–2% of real-data baselines). Expert pathologists rated synthetic images as indistinguishable from real ones. The work addresses data scarcity and privacy concerns in computational pathology.", "questions": "1) Does the model maintain structural coherence across large WSIs, or are there artifacts at slide-level scales?\n\n2) Why did you train UNet instead of DiT? Isn't DiT better for large-scale datasets?\n\n3) Could the framework be adapted for non-H&E stains or multi-modal data (e.g., paired H&E/IHC (immunochemistry) slides)?", "limitations": "Yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1) Dual-Conditioning Approach that combines semantic maps with raw visual crops, avoiding information loss in text/embedding-based methods and preserves diagnostically critical features (nuclear texture, staining patterns) better than prior work.\n\n2) Scalability to Unannotated Data: Self-supervised clustering on TCGA (11,765 WSIs) enables large-scale synthetic data generation without manual labels. Lightweight classifier during inference reduces computational costs by 85%\n\n3) Quantitative metrics (Frechet Distance, IoU) and blinded pathologist evaluations demonstrate clinical realism. Synthetic data nearly matches real data in segmentation performance (IoU 0.71 vs. 0.72 on Camelyon16)\n\n4) Code, pre-trained models, and the generated synthetic datasets will be made available \n\nWeaknesses:\n\n1) Computational Cost: Training requires 4 months on 4× NVIDIA A100 GPUs; clustering TCGA took 48 hours on a 124-core server. 3 months to extract the entire TCGA 224x224 patch embeddings of the high magnification of each 197 WSI on 1 x NVIDIA A100 GPUs with 80GB This may limit accessibility for resource-constrained institutions. \n\n2) Rare Pattern Coverage: Clustering into 100 tissue types may miss extremely rare pathologies (<0.1% prevalence), potentially affecting niche diagnostic tasks.\n\n3) Somewhat limited novelty: existing latent based (semantic mask conditioned) diffusion model applied on large scale data.\n\n4) Downstream tasks focus only on segmentation; diagnostic accuracy (e.g., tumor grading) is not tested.", "was_revised": true, "final_justification": "After reading the other reviews and the rebuttal, I think this is a good paper and deserves the score of 4 (Borderline accept)", "rebuttal": "Thank you for your thorough technical assessment and clear articulation of our key contributions. We're particularly pleased that you recognized how our dual-conditioning mechanism addresses the fundamental information loss problem in text/embedding-based methods, and that you highlighted the contribution of processing 11,765 TCGA WSIs without manual annotations. Your acknowledgment of our comprehensive evaluation strategy confirms our commitment to establishing clinical relevance, and we appreciate your recognition of our open science approach.\n\n---\n\n**WSI-level structural coherence:**\nOur model maintains excellent structural coherence across large WSIs through several design mechanisms. The dual-conditioning approach specifically addresses this challenge: semantic maps provide global spatial organization while visual crops ensure fine-grained morphological consistency at tissue interfaces. Our heterogeneous sampling strategy targets tissue boundaries and transition zones where coherence is most critical.\n\nUnlike semantic-only approaches that can generate artificial boundaries, our visual crop conditioning preserves authentic tissue transitions observed in real histopathology. The pathologist evaluation specifically assessed structural coherence, with synthetic samples receiving comparable scores to real tissue for histological structural detail, validating WSI-level organization essential for diagnostic applications.\n\n---\n\n**UNet vs DiT architectural choice:**\nU-Net outperforms DiT in data efficiency, training simplicity, spatial precision, and computational cost, making it better suited for tasks like medical image segmentation, where labeled data is limited and fine-grained localization is critical. Histopathology presents unique characteristics: (1) limited spatial diversity compared to natural images enables faster UNet convergence, (2) tissue morphology has inherent structural constraints that facilitate learning, (3) diagnostic focus requires fine-grained detail preservation, where UNet's skip connections provide advantages. Our UNet achieved target performance metrics with downstream segmentation reaching within 1-2% of real-data performance.\n\nWe're actively exploring DiT integration for future work, expecting improvements in both synthesis quality and training efficiency. The dual-conditioning framework is architecture-agnostic and should transfer seamlessly.\n\n---\n\n**Multi-modal adaptation capabilities:**\nOur framework is inherently stain-agnostic by design. The core innovation, using raw visual crops rather than processed embeddings, preserves authentic staining characteristics regardless of protocol. For IHC stains, visual crops maintain antibody-specific colorations and cellular localization patterns. For special stains, crops preserve characteristic color distributions essential for accurate synthesis.\n\nCross-modal adaptation requires training on corresponding annotated datasets, but the dual-conditioning architecture remains unchanged. The framework could be extended for paired modality synthesis (generating H&E conditioned on IHC patterns or vice versa), enabling cross-stain validation studies and addressing practical clinical challenges like missing stains or batch effects between laboratories.\n\n---\n\n**Computational accessibility strategies:**\nWhile computational requirements are substantial, they're designed to democratize rather than restrict access through strategic resource sharing. The costs are front-loaded investments enabling broad community benefit:\n\n* **Optimization Strategies:** 8×H100 setup reduces 3-month feature extraction to ~6 days. Cloud deployment makes intensive computations accessible without local infrastructure. Pre-trained models serve as shared resources for synthetic data generation without requiring local training.\n* **Practical Deployment:** Inference requires only standard GPU hardware (1.2s per image on single GPU), enabling real-time generation at most research institutions. The \"train once, deploy everywhere\" model particularly benefits resource-limited institutions.\n\n---\n\n**Rare pattern coverage and representation:**\nOur unsupervised clustering approach naturally accommodates rare morphological patterns through several mechanisms:\n\n* **Comprehensive Coverage:** Processing 634M patches from 11,765 WSIs ensures rare cases are represented rather than overlooked. Feature-based clustering creates distinct groups for morphologically unique patterns regardless of prevalence.\n* **Clustering Methodology:** While textbooks may define only 4 main tissue types (e.g., epithelial, connective, muscle, nervous), the actual visual and structural variability within and across these categories—due to factors like cell density, staining variation, disease progression, and microenvironment—is immense; thus, using 100 clusters allows us to process a much richer set of discriminative visual patterns, capturing subtle phenotypic differences and context-dependent tissue morphologies that are essential for robust training, especially in tasks like segmentation.\n* **Validation Evidence:** The confusion matrix analysis, in the Supplementary File page 8 Figure 5, shows misclassifications typically occur between morphologically similar clusters rather than complete failures, indicating effective rare pattern representation."}]

Author rebuttal:

Meta-review: All reviewers liked the method (dual conditioning) and the quality of the evaluation and recommend acceptance.
Decision: Accept (poster)