D-VST: Diffusion Transformer for Pathology-Correct Tone-Controllable Cross-Dye Virtual Staining of Whole Slide Images
Authors: Shurong Yang
Dong Wei
Yihuang Hu
Qiong Peng
Hong Liu
Yawen Huang
Xian Wu
Yefeng Zheng
Liansheng Wang
Venue: NeurIPS 2025 poster ()
Keywords: Computational Pathology
Whole Slide Images
Virtual Staining
Diffusion Transformer
Primary area: machine_learning_for_sciences

Abstract: Diffusion-based virtual staining methods of histopathology images have demonstrated outstanding potential for stain normalization and cross-dye staining (e.g., hematoxylin-eosin to immunohistochemistry). However, achieving pathology-correct cross-dye virtual staining with versatile tone controls poses significant challenges due to the difficulty of decoupling the given pathology and tone conditions. This issue would cause non-pathologic regions to be mistakenly stained like pathologic ones, and vice versa, which we term “pathology leakage.” To address this issue, we propose diffusion virtual staining Transformer (D-VST), a new framework with versatile tone control for cross-dye virtual staining. Specifically, we introduce a pathology encoder in conjunction with a tone encoder, combined with a two-stage curriculum learning scheme that decouples pathology and tone conditions, to enable tone control while eliminating pathology leakage. Further, to extend our method for billion-pixel whole slide image (WSI) staining, we introduce a novel frequency-aware adaptive patch sampling strategy for high-quality yet efficient inference of ultra-high resolution images in a zero-shot manner. Integrating these two innovative components facilitates a pathology-correct, tone-controllable, cross-dye WSI virtual staining process. Extensive experiments on three virtual staining tasks that involve translating between four different dyes demonstrate the superiority of our approach in generating high-quality and pathologically accurate images compared to existing methods based on generative adversarial networks and diffusion models. Our code and trained models will be released.

D-VST: Diffusion Transformer for Pathology-Correct Tone-Controllable Cross-Dye Virtual Staining of Whole Slide Images
Shurong Yang"
Dong Wei
Yihuang Hu'
Qiong Peng'
Hong 1.2 Yawen Huang²
Xian 2u Yefeng Zheng?
Liansheng Wang"!
Xiamen University, Xiamen, China Tencent Jarvis Lab, Shenzhen, China
Westlake University, Hangzhou, China
(xuancheng, huyihuang, qpeng, liuhong)@stu xmu. edu. en {donwei, yawenhuang, kevinxwu, yef ppfengghenn)@ttencmn com 1swang0xmu. edu. cn
Abstract
Diffusion-basse virtual staining methods of histopathology images have demon- strated outstanding potential for stain normalization and cross-dye staining (e.g.. hematoxylin-eee to immunohistochemistrr).. However, achieving pathology- correct cross-dye virtual staining with versatile tone controls poses significant challenges due to the difficulty of decoupling the given pathology and tone con- ditions. This issue would cause non-pathologic regions to be mistakenly stained like pathologic ones, and vice versa, which we term "pathology Icakage." " To ad- dress this issue, propose diffusion virtual staining Transformer (D-VST), framework with versatile tone control for cross-dye virtual staining. Specifically, we introduce pathology encoder in conjunction with a tone encoder, combined with a two-stage curriculum learning scheme that decouples pathology and tone conditions, to enable tone control while eliminating pathology leakage. Further, extend our method for billion-pixx pixel whole slide image (WSI) staining, we introduce novel frequency-awer
adaptive patch sampling strategy for high-quality yet effi- cient inference of ultra-high resolution images in a zero-shot manner. Integrating these innovative components facilitates a pathology-corres tone-controllable, cross-dye WSI virtual staining process. Extensive experiments on three virtual staining tasks that involve translating between four different dyes demonstrate the superiority of our approach in generating high-quality and pathologically accurate images compared existing methods based on generative adversarial networks and diffusion models. Our code and trained models are available at http://ggghhbb..oomyyygggnoog///
Introduction
Histological stainings are used to colorize tissue specimens, making the near hear-transparent tissue sec- tions visible for pathological observations in clinical diagnostics and research [41]. Different types of dyes manifest different colors in stained tissue and provide complementary information; for example, hematoxylin-eosin (HE) delineate the cellular structures, whereas immunohistochemistry (IHC)
"Co-first authors with equal contribution.
Work done during an internship at Tencent Jarvis Lab. I *Coresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Ultra High Resolution HE Images + Adapiive Sample und Fuse Any Tone Style = Tone- Matching IHC Result
AcaptnolSargle
Tome (HER2 D)
Tima (HEER2 (+)
Different Tone Conditions
Pattology Conrect (HERZ 3-) with different tone styles
East No "Missaie" Arrifact Patholagy Re-Staining
Rewlukka 16,394 16,384
Figure D-VST facilitates efficient (ef. runtime comparison in Table A10), high-quality, tone- controllable, and pathology-correct virtual staining of uutra-high-resolution histopathology images, featuring adaptive patch sampling reduce inference overhead while eliminating mosaic artifacts (cf. Figure 6 and Figure A12;; versatile tone control by different cone-conditionin images; and correct pathological status despite the status of the conditioning images (ef. quantitative and qualitative analysis in Table 3 Figure (AT, and Figure All] HER2 scores: O: no cancerous lesion, and I+, 2t, and 3+: increasing severity of cancerous lesions.
renders protein-specific expression to assist in tumor diagnosis and cancer prognosis [2]. However, current chemical protocols allow only one staining per tissue section; additional tissue sections are required for multiple stainings. This adds to the consumption of often limited tissue samples in clinics. In addition, the staining process is time- and chemical www.rical-cosumingg. Therefore, multiple stainings are
sssorree/labor-intenssss and costly 2 15 36 74 77].
Virtual staining 2 provides a potential solution to multiple stainings a cost-effective alternative to the conventional chemical process. It digitally "translates" chemically stained histopathology images using computational methods. Researchers leveraged generative adversarial networks (GANs) 100 for virtual staining. Despite notable progress, GANs may encounter significant training challenges, such as mode collapse [79.. Recently, diffusion models have demonstrated superior quality to GANs in controllable image generation 19 60 67 90) 91] and started to be applied to virtual staining of histopathology images. These applications can divided into two groups: same- dye stain normalization and cross dye staining. The former addresses the appearance variations in images stained with the same dye (38
39 70), likely originating from variations in institute, chemical 78
material, manual operation I2
80 However, the generalization of these methods to the latter image translation between two different dyes remains to be investigated.
Cross-dye virtual staining translates histopathology images stained with one dye (the source domain) to new images that look like chemically stained with another (the target domain), e.g., HE to IHC. ideally without structure distortion pathology status alteration. However, existing methods 20
53 54, 86) cannot control the staining tones in the target domain 4 leading to unpredictable randomness and significant variations in the tones of the virtually stained images. A potential solution is to condition the staining process (67 (95) with desirable tones. However, is challenging to describe the tones with text. Also, providing the tone condition with image is more complex than giving structure conditions with Canny edges. Specifically, the condiioniiioninn images often contain mixed tone and pathology information. For example, in Figure a(.). using a cancerous IHC image to condition the virtual staining of cancer-free HE image may result in erroneous staining that falsely implies the presence of cancer pathology. We term this issue pathology leakage. To realize effective tone conditioning without pathology leaks, decoupling the tone and pathology information is crucial (Figure ()))
31 33
40
This work presents diffusion virtual staining Transformer (D-VST), a diffusion model with a Trans- former backbone for cross-dye virtual staining of histopathology images (Figure M.. D-VST controls
IIn this work, we refer to the primary color of a type of dye (e.g., the generic pink color of HE staining) as hue, and the color variations of that dye tones (e.g., dark bright pink).

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Correct Staining
Docoupling Pathology
HE (HER2 0)
Tone (HER2 2+)
IHC (HER2 2+)
IHC (HER2 0)
GT (HER2 0)
HFR2 24
(a)
(h)
Pathology Leakage
Figure 2: Illustration of pathology leakage. (a) Without special treatment, conditioning the virtual staining of a cancer-free HE image with a HER2 2+ IHC image may cause leak of the cancerous status from the tone-conditioning image to the virtually stained image, leading to pathology-faul staining. (b) Pathology-correct staining can be achieved by decoupling the tone and pathology conditions. HER2 scores: O: no cancerous lesion, and 1+, 2+, and 3+: increasing severity of cancerous lesions. GT: ground truth.
the staining tone in the target domain without pathology leaks by adopting separate pathology and tone conditions. Concretely, it relies on the source-domain image to be re-stained for pathological structure conditioning and target-domain image for tone conditioning. To our knowledge, D-VST represents the first endeavor realize controlltol rross-dye virtual staining of histopathology images with diffusion models. In addition, we design a two-stage curriculum learning scheme to effectively decouple the model's learning of the pathology and tone conditions in progressive manner. The first stage, pathology extraction, focuses on learning to extract pathology structure information from source-domain images without injecting tone control signals. Then, the second stage, tone injection, adds tone control while diminishing pathology information from the target-domain tone-conditioning image. This involves applying random dropout and Gaussian blur to the tone-conditioning image.
In addition, the virtual staining of large histopathology images like whole slide images (WSIs) requires processing setrr-high-resouutinn data. However, due hardware constraints, directly denoising an entire WSI is challenging for diffusion models. Current methods typically divide a WSI into patches, process them individually, and then stitch them together [I] 39 processed independently, this workaround often leads to discrepancies in color, brightness, and contrast between the stitched patches-rese resulting in the "mosaic" artifact (75). The mosaic artifact may harm or even invalidate the clinical usability of the virtually stained WSI. To address a similar artifact of content discontinuity in text-to- -o--orama application, MultiDiffusion
45 59
64]. As the patches
(3) proposed denoising highly overlapping image patches yielded by a sliding-winde wwiddoww process separately, followed by fusing the denoising directions by averaging the denoised patches within the overlapped regions. However, unlike natural panoramas, WSIs present significant variations in information density across an image's regions. As result, the uniform sliding windows MultiDiffusion may be sub-optimal for WSI virtual staining.
In this work, present efficient and high-quality zero-shot inference strategy for virtual staining of WSIs using diffusion models trained under a prevalent resolution, e.g., 512x512 pixels. Our observation indicates that the mosaic artifact is more pronounced in low-frequency regions of the virtually stained images. Leveraging this insight, we devise a frequency-aware adaptive patch sampling strategy improve the generation quality of low-frequency regions while controlling computational overhead in high-frequency areas. This strategy enables efficient and rapid virtual staining of billion-pixel WSIs without notable mosaic artifacts, significantly enhancing the capability of our proposed D-VST framework.
Our contributions are summarized as follows:
We propose D-VST, a novel Diffusion Transformer (DiT) 60 based model for histopathol- ogy image virtual staining. So far as we know, D-VST is the first diffusion model that realizes tone control for cross-dye virtual staining.
To address the unwanted pathology leakage issue accompanying the tone control, we design an effective, two-step curriculum learning scheme with separate conditioning branches for pathology and tone.
3

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We propose an adaptive frequency aware patch sampling strategy for efficient and high- quality zero-shot staining of billion-pixel WSIs.
Last but not least, we conduct extensive experiments on three virtual staining tasks involving four dyes evaluate our D-VST against up-to-date approaches. We also assess downstream task and perform ablation studies on our method.
2
Related work
GAN-based virtual staining, Conventional methods predominantly employed GANs 7
94 37
100
for virtual staining of histopathology images. A large amount of work [3]
8
21
47
48 49 50
59 61
81
12 85 88) facilitated the transfer of HE to IHC images. M 34
66 showcased generating 41
65|
HE images from formalin fixation and paraffin embedding (FFPE) ones. Moreover,
13 69
implemented GAN-based image style transfer for stain normalization of histopathology images, effectively mitigating color variations. However, GANs are known to be subject to the mode collapse issue 779 and challenging to train. The emerging diffusion models have recently demonstrated superior training stability, generation controllability, image quality, and versatility to GANs.
Diffusion-based virtual staining. Recent advancements in diffusion models 9 60, 67 990 91 have
showcased impressive controllable generation capabilities in image synthesis tasks. Various studies
38
39 70 employed diffusion models for stain normalization of histopathology images. SifinDiff
70
proposed self-supervision to facilitate one-to-one color style transfer. StainFuser [39] leveraged
the ControlNet [95] to implement fast neural style transfer. 40 53
54
861 examined the potential of
diffusion models for cross-dye histopathology image virtual staining. 31 implemented cross-dye
virtual staining without relying on pathological category labels. VIMs (0) introduced lext-controlled
protein markers to facilitate virtual staining across multiple pathological categories. However, these methods cannot control the tones for virtual staining, resulting in unpredictable appearance variations among the re- re-stained images. In contrast, our method controls the staining tone with a target-domain tone-conditionin image.
WSI generation. Virtual staining of WSIss characterized by ultra-high resolution, presents significant
challenges to diffusion models. 26 30 35
84 87 89 966 introduced additional global control signals for direct high-resolution image generation by diffusion models. Yet, this approach does not apply to WSIs due to computational limitations. Alternatively, the sliding window strategy
52
72
17 24 28 46 75 offers a viable means. However, this strategy is impeded by substantial
inference times due to the highly redundant sliding windows with small sliding steps, or subject to performance drop in image quality with large sliding steps. [22] 99 proposed selecting patches at varying timesteps to mitigate long inference duration, yet this mechanism may introduce instability to the generated outcome. To facilitate efficient and high-quality WSI virtual staining, we propose a novel adaptive patch sampling strategy based on image frequency variations.
3 Method
The framework of our method is shown in Figure B3 (left). Primarily, D-VST tailors and extends the Diffusion Transformer (DiT) (60) in PixArt-a [9] its denoising Transformer. A pathology encoder encodes the source image into pathological structure embeddings, which are injected into the denoising Transformer as the structure condition after concatenating with the noise latent. Meanwhile, a tone encoder (which we use the pretrained Vision Transformer (VIT) [18] in CLIP (63)) encodes an auxiliary, cone-conditioning image from the target domain into tone embeddings. The tone embeddings are also injected into the denoising Transformer via multi-heat tone attention. The denoising Transformer integrates the structure and tone conditions and denoises toward a re-stained target histopathology image (latent).
The pipeline of D-VST proposes innovative designs for both training and inference schemes. To prevent pathology leakage from the tone-conditioning image, we decompose the intricate virtual staining task into curriculum learning (4) streamline for progressive training [68]. To eliminate
the mosaic artifact at a low computational cost while staining high-resolution images, we propose a frequeney-awen aware adaptive patch sampling strategy for efficient inference.

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Winully daine Image
Sample
1 Basic Block
Scale
Wiie Peedforward
VAR
High-Degeeee
Scale, Shift
FFT
Denoising
Basic Block
Transformer
Split
Basic Block
Muiti
Tome Amention
69
Basic Block
Stalle
D-VST
Mustt-Hlead Spotial Attention
Tows
Puthology
Frceeder
Scale, Shift
Cli
Noie likeed
S Paintiie Adding
Charnel Uineal
Timesiop
(c) tvutlarpol
Tehhologicll
image
trage
Froacs Weigls
FFT. Fast Fourier Trandeem
Figure 3: Left: framework of the proposed D-VST. Right: frequency-aware adaptive patch sampling. (a) FFT-based local frequency computing. (b) Covering the entire image with tiled, non- overlapping patches. (c) Additional patches sampled according local frequency. We show only nine patches here to illustrate that patches are more likely to be sampled from low- than high- high-frequency regions. In fact, all sampled patches cover seven times the area of the input image. (d) Individually denoising each patch. (c) Fusing by averaging overlapped regions.
1. Pathology Extraction Training
2. Tone Injection Training
N
Target Domain
MSSEVLB Loxe
Foodfforwrdd
Feodforward
CLIP
Dospeet par- enbedding
Multi-Heud
VAE
Tome Attention
Spatial Attention
Gaussiun Blur
Spatial Attention
Noise
Pathloggy A
Weight
Chunnel Concat
Domain
Encodar
Tonning Waight
Pointwisc Adding
imagg
Figure 4: The two-stage curriculum learning scheme decouples pathology and tone conditions. Initially, only the pathology condition is input to the model, emphasizing accurate pathology extraction while ignoring the significant hue difference between the source and target domains. Next, we add tone condition via cross attention, with random dropout and Gaussian blur to diminish pathology leakage from the tone-conditioning image.
3.1 Curriculum learning scheme decoupling pathology and tone conditions
As introduced in Figure 2 training with mingled pathology and tone controls often causes pathological status to leak through tone conditioning, leading to incorrect staining outcomes. This occurs because the model confuses the purposes of the conditioning images and mistakes the tone-conditioning image for the source of the pathology status. Thus, the key to effectively preventing pathology leakage is to decouple pathology and tone conditions by making the model lear both control signals precisely. For this purpose, we design a novel two-stage curriculum learning 4 scheme. In the first stage, pathology conditioning is first learned alone. Then, tone conditioning is introduced in the second stage for further joint training.
Training stage 1: pathology extraction. Figure 4 (left) illustrates the first training stage. In this stage, we only feed the pathology condition into the model via channel-wise concatenation with the noise latent. Meanwhile, the denoising Transformer includes only the spatial attention and feedforward modules. The training goal to denoise the corrupted target-domain image (obtained by adding Gaussian noise as in typical diffusion processes) in the latent space of pretrained variational
5

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guided by the pathology structure embedding extracted from the paired source-domain image. We follow [60] to train the model with the hybrid mean squared error (MSE) and variational lower bound (VL.)) losses [55] where the former learns to predict the sampled noise and the latter learns variances of the reverse diffusion process (ef. Appendix for more details). Thus, the model learns to effectively extract and utilize the complex pathology information in the pathology-conditione between the source and target domains.
autoencoder (VAE
+
image in this stage, ignoring the significant hue difference
The pathology encoder uses lightweight convolutional network with 4x4 kernels, 2x2 strides, and four layers of 16, 32, 64, and 128 channels. It encodes the embedding of the same shape as the noise latent. The embedding is concatenated with the noise latent along the channel dimension and input to the denoising Transformer. Compared to the ControlNet [95] architecture used in StainFuser [39], our lightweight pathology encoder is equally effective in capturing detailed pathological information while substantially reducing model complexity and computational cost.
image into an
Training stage 2: tone injection. The second stage introduces tone conditioning into the model (Figure A (right)). A random patch from the same WSI but different from (thus not paired with) the target-domain image is used for tone conditioning. It provides a precise tone style but not necessarily pathology information of the target image (different patches of a WSI may present distinct pathological statuses). To emphasize tone features while minimizing pathological structural information, we first apply Gaussian blur to the tone-conditiniing image. Then, we utilize the pretrained ViT [18] in OpenAI-CLIP 63 to encode the blurred image into a tone embedding. The OPenAI-CLIP VIT was trained on massive data of versatile colors, making it a proper tone encoder for optical pathology images (cf. Appendix for comparison to the PathCLIP (76).. Next, in the middle of the frozen feedforward and spatial attention modules, we insert a multi-head tone attention module into each unit block of the denoising Transformer trained in the first stage. Lastly, using cross-attention, we inject the tone embedding into the denoising Transformer via the inserted tone attention modules. To further reduce pathology leakage, we apply random dropout to the tone embedding by replacing it with a zero embedding. The dropout rate is set to 20% according to preliminary trials The same training losses as in the first stage are used. Ablation studies confirm the efficacy of the tone encoder's components (Table
3.2 Frequency-aware adaptive patch sampling
From the "No-overlap" column of Figure 6 we observe that the mosaic artifact is more pronounced in low-frequency regions of the virtually stained images. This occurs because, unlike areas with complex textures that contain abundant clues guiding the virtual staining process, low-frequency regions require more overlapping patches for consistent denoising results. To improve the generation quality of the low-frequency regions while simultancously controlling computational overhead, we propose frequency-awan
adaptive patch sampling strategy. As the premise, we divide the input image into a grid of n x n squares. We set = 32 through grid search (cf. Appendix). Then, for each square I, we compute the natural logarithm of the magnitudes of its fast Fourier transform (FFT; Figure 3 (a)):
L log (abs (FFT(I))].
(I)
Next, we calculate the pixel-wise mean of L, denoted by I, as the frequency statistic for the square. Finally, we convert to sampling probability by:
(mmax + pmin 1
(2)
(Imax + Imin 4))
where mmax and uii are the maximum and minimum / values of all n² squares, (!max + Juin 4) makes low-frequency squares more likely to be sampled, and a E = is hyperparameter controlling the difference in sampling probabilities between low- and high-frequency squares.
"In preliminary experiments, when applying the VAE of Stable Diffusion (SD) 1.5 67 to pathology image reconstruction, we obtained an average peak signal-to- noise ratio of ~28 dB, comparable to the performance on natural images (~~25 dB). Therefore, we use the SD 1.5 VAE in this work.
Further optimization via rigorous ablation study may lead to better performance; leave it for future work.

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To ensure the entire input image is stained, we first fully cover the image with tiled, non-overlapping patches (Figure (Figure[(right) (right)-(b); note the patches are larger than the squares for FFT). Next, we sample additional patches according to the sampling probabilities in Equation (2) square 7 is first selected according to pi, and a random pixel within is subsequently chosen as the center of an additional patch. This way, more patches are adaptively sampled where necessary, whereas fewer patches are sampled from high-frequency regions to maintain reasonable computational costs. In this work, we define an integer / as the ratio of the total area of tiled and sampled patches to the area of the stained image. The larger 3 is, the more patches are sampled (the special case of no-overlap sliding windows has 3 1). The patches step, and the denoised patches are fused in overlapping areas by averaging (Figure ( (right)-(d) and (e)) [3]. Experiments show that compared with MultiDiffusion [31, our strategy improves the virtual staining quality with only 12.5% computational overhead (B = 8 versus 64; cf. T2ble
processed separately in every denoising
Experiments
Datasets and evaluation metrics. We evaluate D-VST on three datasets to comprehensively validate its performance on the virtual staining of various dyes. Regl211 [6]] comprises 2,592 pairs of includes 5,098 pairs of aligned images S contains 5,292 matched image pairs for HE to multiplex immunohistochemistry (mlHC) staining (HF2mlHCC. These datasets include two organs/cancer types: breast cancer (HE2IHC and FFPE2HE) and colon cancer (HE2mIHC). We use all datasets' official train/testvvaliaation splits. Unless otherwise specified, we report performance on cropped images of 1024x 1024 pixels as in [5] 34 61]. and use random patch from the same WSI but not overlapping with the target image for tone conditioning. Note that the tone-conditions condiiiitn and target images may present different pathological statuses, and the no-overlap requirement ensures no structural leak.
registered images for HE to IHC staining (HE2IHC), 34
for FFPE to HE (FFPE2HE) staining, and HEMIT
we employ the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) for quality assessment of the virtually stained images. However, for paired histopathology images stained with different dyes, perfect pixel-to-pixed matching is practically impossible even after registration (see Appendix for more explanation). Therefore, for an appropriate evaluation, we additionally employ three metrics that are more perceptually relevant than the conventional SSIM and PSNR: deep image structure and texture similarity (DISTS) [66. Fréchet inception distance (FID), and kernel inception distance (KID) 6).
Following previous works 5
34
61,
Implementation details. All experiments are conducted in Python 3.10.0 with PyTorch 2.0.0 [88] on a GPU with 80 GB of memory. We follow 99 to use DiT-XL/2 [60] as the base network architecture for our denoising Transformer, and the pretrained parameters from 9 We employ the AdamW [44] optimizer with a learning rate of 10-5 and batch size 32. We train for 30,000 steps for the pathology extraction stage, and an additional 10,000 steps for the tone injection stage. The diffusion time 7' is set to 1000. Our model trains and infers at the resolution of 512x512 pixels. For virtual staining of larger images, we use the model to denoise 512x512 patches sampled according to the proposed frequency-awan adaptive patch sampling strategy (cf. Section 3.2). and fuse the patch-wist outcomes by averaging overlapped regions [.. Unless otherwise specified, we set a = and 8 = 8 for the adaptive sampling. Our code and trained models are available at https://github.cc http://ghhhhbooooy/ggsuuuuroo/// wow//yngsshroongDD-..
Comparison with state-of-the-art (SOTA). We compare our D-VST with classical GAN-based
image translation methods: CycleGAN [100, pix2pix (37 and pix2pixHD [83]: medical image diffusion model: SynDiff [56]; and SOTA GAN/diffusion models specialized in histopathology image virtual staining: [34] and /Saanuiier
(39]. The comparisons with 34 and [6]] are exclusively
on the FFPE2HE [34] and HE2IHC [6] datasets, respectively, since the two methods were designed for the specific tasks. As shown in Table D. achieves the best performance for all metrics on the HE2IHC and HE2mIHC datasets, indicating that the histopathology images virtually stained by D-VST are superior both perceptually and structurally. On the FFPE2HE dataset, D-VST yields slightly inferior PSNR and SSIM to the best numbers, yet is still competitive. We conjecture this is because the micro-level correspondence (pixel- and structure-wise) between the paired images in this dataset is not as good as the other two, Notwithstandingg D-VST again achieves the best performance
7

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Table Evaluation of various methods on three
Table 2: Evaluation of sampling strategies for zero- shot staining of images larger than the training resolution of diffusion models. /9 is the ratio of the total patch area to the area of the virtually stained image. Given a fixed patch size, the larger /3 is, the more patches are sampled, thus the higher compu- tational cost.
different datasets and virtual staining tasks.
Mothod
DISTSI
FIDL
KIDJ
PSNRT
SSIME
HR:3HC [6]
Pix.pix
0.192
47.77
0.0257
18.04
0.401
120126
Pix]pixID
83
0.191
41.77
0.0123
18.06:13.33
0.386:20.125
CycleGAN
100
0.212
40.91
0.0062
17.014+3534
0. 11
[6]
0.174
33.92
0.0058
18.0245706
0.385-20125
SynDet 55
0.348
225
0.2282
18,0911-040
0.001
StuinFuser [9]
D:VST
0.255
0.154
33.16
104.5
0.0791
0.0055
18.71
17.30:4-:949
0.407 1.1136
Sample stralcey
DISTS! FID, KID. FSNR
HE2HIC [6]]
SSIME
Ps 83
Pic
0.133
0,170
HE2m/HC [5]
28.92
29.95
0.0025
0.0086
27.26613:00
27.05++-3996
01816--0000
SpoDiffusion
MultiDiTusioo D-VST
No-overlpp
0.1394 37.748 0.0022 1801
0.2256
0.1566
34,661 0.006]
18.0933577
11.08128155
0.297:10.177
0,406:20156
CycleGAN
100
0,300
83.16 0,0365
10:15:1:665
0.520=t0.049
0.1548
33.162 0.0055
18.11
0,407:10.136
SynDiff 56
0.318
316.1
0.4057
20:05-+1.435
0.709-+0.00
HE2m/9C
StainFuscr
39
0,289
99.20
0.0690
20, 20.27::2057
0.309:20.04p
Na-overlap
0.1063
21.366 0.0021
27.722:554
0.85210048
D.VST
0,106
20.36
0,0016
28.01114:123
0,861
24,04
SpaDiffusion 2
0.4285
111.35 0.0937 10.8743711 0.373:10.09
PFPEZHE (34)
Multiiifuriio
g
64
0,1069
20.365 0.0016 27.96:::2260
0.858 1:045
Pix2pix [7
0.109
17.88
0.0008
T8 18.220120000
0.336:20.133
D-YST
0.1062
20.361 0.0016 28.01+44121
0.861-6044
Pix2pixHD> [3
0.091
15.26
0.00088
19. 19.00.--20066
01586--0
FFFE27E [31]
CydeGAN 100
0.171
35.36
0.0087
14:47::20055
0.363-20.15
No-overlap
0,0929
16,403
0.0012 17.002210
0.31720120
34
0.126
31.56
0.0101
19.86:12.082 0.644
SpoDiffusion
48.281
15.4111400
0.46711..1
SynDin
12:15117757
MulsDiifrasion D-VST
t
0.0910
14.960
0.0007 17.87+22165 0:531
StainFuser 39
0,200
64.98
0.0301
12.46.118851
0.263-16.157
0,1900
14.263 0,0005
17.98:22166
0.538
D-VST
0.090
14.26
0.0005
17.988+2218
01538-60.117
HE/FFE
Tone
Rewlt
Tome
Result 2
Tone 3
Result
GT
Case
Case
Figure 5: Qualitative results for tone control. Case 1 is from the HE2IHC [61] dataset, whereas Case 2 is from the FFPE2HE (34) dataset. Despite substantial discrepancies in pathological status between the pathology- and tone-conditioning images, the pathological status is correctly transferred from the pathology-conditoonin
images to the virtually stained ones. For example, in Case I, the HER2 score of the HE image is 3+, while the scores of Tone 1, 2, and 3 are all O. GT: ground truth.
for the three perception-orient -oriente metries (DISTS, FID, and KID). general, D-VST demonstrates strong capabilities in virtually staining high-quality histopathology images of versatile dyes.
Tone control and downstream task. As the HE2mIHC (5] dataset has undergone color nor- malization and thus cannot provide varying tone conditions, we conduct qualitative tone control experiments on the other two datasets. As shown in Figure 5 when conditioned on histopathol- ogy images of various tones of another dye, the virtually stained images exhibit varying tones matching the conditiooi images while maintaining the same pathological status as the source hawhooooy-oonditioonnn.
images. For example, Case illustrates that even when there are substan- tial discrepancies in pathological status between the pathology- and con-connitioning images, the pathological status is still correctly transferred from the pathology-conditoonng image to the virtually stained ones. These observations indicate that our D- D-VST can effectively prevent pathology leakage for cone-conditioned cross-dye virtual staining. We provide more visualizations and comparisons with other methods in Appendix.
To further quantitatively validate our method's effectiveness in pathology leakage prevention while using image-based tone conditioning, we perform downstream classification task on HE2IHC [I]. Concretely, we further split the 600 official test pairs into a sub-train and sub-test set of 480 and 120 pairs, respectively. Then, we train ResNet50 (2] classifier on the IHC images of the sub-train set, with labels corresponding to the four HER2 scores (HER2 O: no cancerous lesions, and 1+, 2+, and 3+: increasing severity of cancerous lesions, with higher scores indicating more pronounced lesions and more advanced disease stages). Next, for each HE image in the sub-test set, we randomly select an IHC image in the sub-test set that is not paired with the specific HE image as the tone condition for virtual staining. Lastly, we apply the trained classifier to the virtually stained IHC
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Table 3: Evaluation of downstream classification
Table 4: Ablation study on the HE2IHC 66
task on the HE2IHC [6]] dataset.
dataset with both image generation and down- stream task metrics.
Method
ACCT
FI7
Precision
Recal
Pix2pix [37]
0,8750
0.8755
0.8916
0,8779
Metric ACCT
w/o Droposi
w/o Ganssian wio Curriculom
D-VST
Pix2pixHD 83
0,8500
0.8508
0.8655
0.8384
0.9250
0,783
0.7667
0.5417
CycleGAN
[T0
0.5883
0.5609
0,5600
0,5512
D.9265
0.7710
0.7672
0.8917
0,8925
0,8930
0.8847
Precisiont
0.9292
SynDiff (56
0.2167
0.0884
0,3025
0,2589
Recallf
0.9250
0,7585
0.7645
0,9388
StainFuser
0.7250
0.7324
0.7331
0,7230
DISTS!
BT380
D22372
0.1729
0.1548
D-VST
0,9417
0,9430
0.9470
0.9388
FID,
35,815
55,148
38.957
33,162
Real ITCC images
0.9500
0.9506
0,9530
0,9458
PSNRT
KID]
17281+160
0.0070
15.852456000
0.0290
17.587-1-69
18,113-1884
0.0055
SSIM
0. 0.3904
0,4361 0:4012=0.117
0.4074=0
FFPE
No-overlop
Muhiiirfusioo
SpotDiffacion
Ours
GT
Figure 6: Comparison of sampling strategies for zero-shot virtual staining of large histopathology images. Red ellipses outline regions where the mosaic artifacts are prominent (best viewed zoomed in). GT: HE ground truth. More is provided in Appendix.
images. Intuitively, if the pathology leakage happens, the classification results would notably deviate from directly classifying the real IHC images in the sub-test set. T shows that our classifier achieves high performance on real IHC images, and more importantly, the performance on virtually stained IHC images by D-VST is also high and closely matches that on real ones. Meanwhile, D-VST obtains substantially better results than the compared methods. These results demonstrate that our method successfully decouples pathology and tone information for cone-conditioned cross-dye virtual staining, and its virtually stained images are of high quality for potential clinical use.
Sampling strategy for zero-shot virtual staining of large histopathology images. As described in the implementation details, our model trains and infers at the resolution of 512x512 pixels. For the virtual staining of larger images, we use the model to denoise 512x512 patch samples, followed by patch fusion. Here, we compare several sampling strategies in terms of performance and computational cost: no-overlap (sliding windows without overlap), SpotDiffusion 22 (sampling sliding windows that vary with timesteps). MultiDiffusion [3] (sliding windows with a high overlap ratio), and our proposed frequency-awand evaluated on the same network (our proposed), which takes about 1.081s and 6.4 GB memory to infer a patch of 512x512 pixels on our hardware, their relative computational costs can be compared by | values. We conduct quantitative evaluations on the HE2IHC, FFPE2HE, and HE2mIHC datasets, generating images of 1024x 1024 pixels. As shown in Table 2 although no-overlap and SpotDiffusion incur the least computational cost, their performance is the worst. MultiDiffusion improves all evaluated metrics, though at 64 times the inference cost. In contrast, our strategy achieves the best performance for all metrics the three datasets, while incurring only 1/8 of the inference computation of MultiDiffusion. For more insights, we additionally evaluate MultiDiffusion with the same 3 8 as ours on HE2IHC. Its FID degrades from 34.66 to 34.99, markedly inferior to our 33.16. These results demonstrate that our frequency-awar adaptive sampling strategy is not only highly efficient but also capable of boosting the quality of virtual staining.
adaptive patch sampling. Since different sampling strategies
Figure 6igure shows example results by the compared methods for qualitative analysis, virtually stained at the resolution of 2048 x x2048 pixels. No-overlap exhibits noticeable mosaic artifacts, whereas SpotDiffusion presents anomalous tones aacounticonng for their unsatisfactory quantitative results. With only 1/8 of the computational cost of MultiDiffusion, our strategy produces images of equal visual quality to MultiDifiusion. We have also applied D-VST to the virtual staining of WSIs of 0.2-1.3 billion pixels (16,000x 15,000 to 40,000x32,000 pixels). However, far we know, no suitable WSI dataset is currently available for reliable quantitative evaluation at scale. Therefore, we only show the qualitative results in Appendix for an observational study.
Ablation studies. In Section 3.1 we have proposed two-stage curriculum learning, Gaussian blur of tone-conditioning images, and random dropout of the tone condition to realize pathology and
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tone decoupling and prevent pathology leakage. Here, we ablate one of them at a time (denoted by w/o Curriculum, w/o Gaussian, and w/o Dropout) to study their efficacy on the HE2IHC dataset using both image generation and downstream classification metrics. Table 4 shows that removing any of them leads to overall declines in all metrics (the only exception is the SSIM w/o Gaussian). Especially, removing curriculum learning results in the most significant performance drops in three of the four classification metrics. These results suggest that these components, especially the two- stage curriculum learning scheme, effectively boost the performance of histopathology image virtual staining. This achieved by improving the perceptual quality via effective pathology and tone condition disentanglement, thus fulfilling design.
The Appendix includes further experiments determining the values of the hyperparameters a and in the frequency- adaptive patch sampling (cf. Section 3.2)
5 Conclusion
This work presented D-VST, a diffusion Transformer based framework for efficient, high-quality, and pathology-preserve cross-dye virtual staining of histopathology images with up to more than a billion pixels. Extensive experiments on three virtual staining tasks involving four types of dyes and downstream cancer status classification task validated D-VST's promising performance. Facilitating efficient cone-controllable virtual staining, D-VST has the potential make broad impact on algorithm development and the clinical pipeline of histopathology image analysis.
Limitations and future work. Our D- facilitates efficient virtual staining of ultra-hight www-hhiihh-hhhhee resolution histopathology images like WSIs by the proposed frequency-aware adaptive patch sampling strategy. However, its inference speed is still constrained by the multi-step denoising process inherent in diffusion models 332. Inspired by 42 51 73
we plan to optimize the denoising scheduler and reduce inference steps by flow rectification and consistency models, and further reduce computation overhead and accelerate inference by model pruning and distillation retraining (93]
Obtaining paired cross-stain training data can be challenging and costly in real-world workflows, which may limit the scalability and applicability of D-VST. While such data can improve virtual staining performance with paired correspondence, unpaired data is substantially more scalable due to orders of magnitude larger amounts. In future work, we plan to explore benefiting from both the scalability of unpaired data and the quality of paired data via combination of D- D-VST and approaches 92 like CycleGAN-Turbl (27 57), which enable diffusion models to learn from unpaired data.
In this work, we have attempted to fine-tune the VAE alongside the Diffusion Transformer in our experiments but obtained mixed results (similar PSNR and SSIM with poorer DISTS, FID, and KID), likely due to the limited training data. In the future, we plan to explore whether substituting the VAE with a performance.
counterpart would further enhance our framework's
Lastly, future work will investigate D-VST's benefits for downstream segmentation tasks 97
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 62371409).
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A1 Training losses
We briefly describe the losses for training our framework. denoising diffusion probabilistic models (DDPMs) [32]. forward process gradually applies noise to real data ro: q(z,) (00) = Na vā,zo, (1 - dz ).) where t is the time step and constants aj are hyper-parameters. Ap- plying the reparameterization trick, It can be sampled by
Vorzoo + v1 Orεt, where er ~ -N(0, I). Inversely, a model learns the reverse process to gradually restore the noise-corrupted real data by po (2)_
((ps(zz (z))), using neural networks to predict the statistics of pa- The model is trained with the variational lower bound (VLB) of the bog-liieldoood of To. which can be written as (omitting training-irrelevan term): CVLB
log
L, PRL. (9(x;-1 zo) (1 (z+)), where DKL. is the Kullback-Leibler (KL.) divergence loss. By parameterizing μ4 as a noise prediction network cg, the model can be trained with the mean- squared error loss between the predicted noise e0(x)) and the ground-truth sampled Gaussian noise e:: LMSF; = (x+) - ezlle. Meanwhile, to learn the covariance Eg, the full LVLA needs to be optimized. We follow (55] to train ep with CMSE, and Eg with LVLB- Since the training losses are not a focus of this paper, we refer interested readers to [55] for more details.
A2 Justification for non-perfect paired data
In virtual staining tasks, paired images are typically obtained from consecutive tissue sections and algorithmically registered. Although the alignment does not perfectly match all pixels, most are closely aligned and thus valid for structural correspondence learning. Before ours, many methods
successfully trained their virtual staining models the datasets used in this work [5] 34 61].
In the main text, we conjectured that D-VST's lower PSNR/SSIM versus + on FFPE2HE "is because the micro-level correspondence (pixel- and structure-wist wise) between the paired images in this dataset is not as good as the other two." Although this misalignment is inherent in all datasets used in this work due to the consecutive slicing and chemical staining process, we visually find it more serious in the FFPE2HE dataset. To quantify the structural (mis)alignment between paired images, we resort to the following procedures. We apply the Canny edge detector and compute the Hausdorff distance, intersection-over-une source and target images. Intuitively, lower Hausdorff distance and higher IoU and Dice metrics indicate better structural alignment. As shown in Table A5 FFPE2HE [34] shows consistently worse alignment metrics, supporting our conjecture. In particular, the Hausdorff distance measures the maximum deviation between two point sets, highlighting the worst-case alignment errors. Thus, the substantially larger Hausdorff distances indicate more extreme misalignments.
(IoU), and Dice similarity between edge maps of the
Table A5: Quantification of structural (mis)alignment between paired images in the HE2IHC and FFPE2HE datasets using Hausdorff distance, intersection-over-unioo (IoU), and Dice similarity.
Datasets
Hausdorfi
loUt
Dicet
HE2IHC 30.92 0.000
0.260-E0.041
FFPE2HE
36.63.133.13
0.138 0.242-10.016
A3 Additional experiments
Choice of tone encoder. For the tone encoder, we experimented with three image encoders: one
pretrained in OpenAI-CLIP [63) on massive data of broad spectrums; one pretrained in PathCLIP (76) on 207K high-quality pathology image-caption pairs; and one pretrained in UNI [T0] on over 200 million pathology HE and IHC images. Table 66 presents their performance on the HE2IHC dataset 66.. UNI and OpenAI-CLIP demonstrate comparable performance, and clearly outperform PathCLIP metrics while remaining comparable in PSNR and SSIM. We conjecture that the difference in performance may be partly attributed to the function of the tone encoder. On the one hand, while PathCLIP may be better prepared for downstream tasks on pathology images (e.g.. classification), the tone encoder focuses more on color perception. As a result, OpenAI-CLIIP may be more suitable due to its more significant amount of training data that inherently includes more
in
A24

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versatile color variations. Thus, we use OpenAI-CLIP within our D-VST framework. On the other hand, while UNI offers strong pathological feature extraction, OpenAI-CLIP suffices for capturing tone information in D-VST. Hence, pathology foundation models trained on multi-stains like UNI are also excellent choices for the proposed D-VST framework. However, it important to note that even when using PIthCLIP the tone encoder, our performance remains competitive with other methods in Table the main text.
Table A6: Performance comparison of using PathCLIP 76 UNI 10 and OpenAI-CLIP 63 the tone encoder on the HE2IHC [61] dataset.
Tone encoder
DISTS
FIDI
KID|
PSNRT
SSIMT
PathCLIP
0.190
40.01
0.0086
17.49-3.717
0.411
UNI
0.157
33.13
0.0048
18.03++3.842
0.404:t0.130
OpenAI-CLIP
0.154
33.16
0.0055
18.11
0.407-10.136
Influence of hyper-parameter n. for square split. The input images be re-stained are divided into grid of x squares for local frequency estimate. We vary n from 4 to 128 and show the FIDs {16, 32, 64), the results are the best and stable, whereas being too small or large deteriorates the performance. The empirical guideline is to select properly so that each square contains enough pixels for a reliable frequency estimate but not too many pixels to remain local estimate. We use 32 in our paper.
for HE2IHC [61] Table A7 When
Table A7: Performance in FID with varying values for the hyper-parameter n on the HE2IHC [6] dataset.
4
8
16
32
64
128
FID4 33.452
33.444
33.267
33.162
33.323
33.548
Ablation study on and 3. and / are important hyyeer-arameteee in our proposed frequeney- aware adaptive patch sampling. Concretely, controls the sampling probability discrepancy between low- and high-frequency regions, whereas (3 trades off between computational cost and image quality. Figure 77 presents the impact of varying and 33 values on the model performance focused on FID, whose variations are more evident among the perception-oriented metrics.
The impact of a FID
The impact of f on FID
FID
33.6
33.5
37
33.4
36
33.3
33.2
33.1
34
33.0
33
-1
0
0.2
0.5
1
2
3
6
4
8
16 B
Figure A7: Performance plots (FID) of varying values of the hyper-parameterd (left) and 3 (right) on the HE2IHC [61] dataset. Note that the labels for the horizontal axes are indicative and do not strictly follow the actual intervals between values.
As shown in Figure A7 (left), the best performance occurs at 1, while extreme values (a 0 or 6) degrade performance. Notably, a = O, which is effectively the random patch selection, yields the second worst performance. In addition, experiment with sampling more patches in high- high-ffreuency
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Table A8: Evaluation of various virtual staining methods on two external validation datasets (and virtual staining tasks). For all methods, the models trained for the corresponding tasks in the main text are directly used here for inference without any tuning.
Method
DISTSL
FIDI KIDI
PSNRT
SSIM
Ex1-HEIIHCC [6]
Pix2pix 37
0.2923
147.02 0.1065
16.632-4:3442
0.3250-H0.120
Pix2pixHD
83
0.2792
104,96
0.0597
16.361=3.390
0.3419::0.128
CycleGAN
100
0.2823
149.61
0.1168
16.918::3484
0.3410::.117
[6]
0.2657
104.79 0.0796
15.708---2.234
+-0.100
SynDiff [56]
0.3584
251.49
0.2500
16.700:1:33356
0.3552.1:0.099
StainFuser 39
0.2634
156.55
0.0932
16.131+3.336
0.3436-+-.143
D-VST
0.2628
88.287
0.0396
16.713
1...01:10.140
EX-FFPE2HE [34
Pix2pix [37
0.2066
44.919
0,0271
15.780=1853
0,4580=10.07
PiX2piXHD
83
0.1708
28.332
0.0139
17.726:1
0.4789
CycleGAN
100
0.2913
99.343
0.0780
11.778=2.055
0.2774 125
34
0.1908
51.966
0.0342
18.975=1140
0.5932:+0.073
SynDiff [56]
0.3420
154.37
0.1344
6.8810=2.005
0.2219
StainFuser 33
0.2153
71.887
0.0408
1..538 0.2326:10.093
D-VST
0.1521
27.488
0.0108
17.17911.036
0.4591
regions by setting a = -1. The performance worse than that of random sampling (a = 0) and our low-frequency-prefereee sampling (a = 1), validating that mosaic artifacts impact low- oow-frquueccy regions more. We use a for experiment comparison with other methods in the main text.
As for B, we study its impact with a fixed to 1. Figure A7 (right) shows that increasing the number of sampled patches rapidly improves the performance, which is reasonable, until the saturation at 3 = 16-with a similar performance to / = 8. Considering that 3 = 16 doubles the amount of computation of B = 8, we set for performance comparison in the main text.
Validate sampling probability design. To validate our design of sampling probabilities in Eqn. (2), we use the medium/ / of images as the threshold for low- and high-frequency patches. It turns out that our method samples 69% of patches in low-frequency squares versus 31% in high-frequency ones. These numbers indicate that the sampling probabilities work as designed.
External validation. To further evaluate the generalizability of our method, we conduct external validation on two datasets: (1) the external test set from [6], comprising 285 HE-IHC image pairs stained with SP3 [14] CB 11 (62) antibodies (Ext-HE2IHE) and (2) the external test set from [34], containing 1,398 FFPE-HE image pairs (EXt-FFPE2HE). It is worth noting that for the external validation, we directly use the models trained for the corresponding tasks in the main text without further tuning. As shown in Table A8 D-VST again achieves the best performance for the three perceptiion-oriented metrics (DISTS, FID, and KID) performance for PSNR and SSIM (ranking top one to top three among all compared methods). These results consistent with those presented in the main text, demonstrating D-VST's strong generalizability in cross-dye virtual staining of histopathology images.
both external test datasets, and competitive
Additional comparison with existing methods. In this section, we compare our D-VST with two additional state-of-the-art approaches to virtual staining, PSRVS [%]] and DeepLIIF 23] on the HE2IHC [6]] dataset. The former belongs to diffusion- models, and the latter to GAN- based. The results are shown in Table A9 With the due caution that these two methods may not be fully optimized for this task, we can see that D- VST substantially outperforms PSRVS and DeepLIIF in DISTS, FID, and KID, and is comparable in PSNR and SSIM.
Additional qualitative results. We present additional qualitative tone control results on HE2IHC and FFPE2HE in Figure A9 under settings consistent with Table and Figure 5 of the main text. We also show qualitative visual comparisons with other methods Figure A and Figure/ATTJunder the same settings. Figure A12 displays more qualitative comparisons between different sampling strategies
A26

---

Table A9: Additional performance comparison with PSRVS 98| and DeepLIIF
23 on the HE2IHC
dataset.
Method
DISTS.
FIDJ
KIDJ
PSNRT
SSIM
D-VST (ours)
0.154
33.16
0.0055
18.1143.874
0.407#0.
PSRVS
0.422
230.3
0.2283
17.94#4.193
0.396+
DiepLiiF
0.305
140.3
0.1379
17.9643.713
0.418+0.129
for zero-shot virtual staining of large histopathology images at the resolution of 2048 x 2048 pixels. These results qualitatively demonstrate that D-VST can (1) precisely control the tones of the cross-dye virtually stained histopathology images without pathology leakage, (2) generate high-qualit, large histopathology images in an efficient manner, and (3) support virtual staining of ultra-high high-resoluuiion WSIs, validating the versatile generative capacity and generalization ability of D-VST.
WSI validation. As far as we know, no suitable WSI dataset is currently available for reliable quantitative evaluation at scale. Therefore, we mainly show the qualitative results Figure A13 Figure A14 and Figure A15 for an observational study. Figure A13 shows ultra-high-resolution
(16,384x 16,384 pixcls) HE2IHC virtual staining examples. Lastly, Figure [114 and Figure A15
showcase virtual staining results of WSIs (16,000x 15,096 and 40,000 32,496 pixels) from Ext- FFPE2HE, each conditioned with two different target tones. We have asked two board-certified pathologists to blindly rank the WSIs virtually stained by our D-VST, representative GAN-based method (pix2pixHD (83), and two diffusion bassed methods (SynDiff [56] and StainFuser [39 ) considering image quality and pathology correctness. The evaluated WSIs include two HE2IHC and two FFPE2HE images, shown in in Figure [A3] Figure A14 and Figure A11] respectively. The mean ranking is: D-VST (1.0), pix2pixHI (2.5), StainFuser(3.0), and SynDiff (3.5), demonstrating D-VST's superior virtual staining quality.
As for timing, record the generation times for the WSI in Figure of the main text (16,384 16,384 pixels; HE to IHC) using representative GAN-based method (pix2pixHD (83), two diffusion-basee methods (StainFuser [39] and SynDiff' (56)), and our D-VST. To avoid the unwanted mosaic artifacts, we implement the MultiDiffusion 33 sampling strategy for the compared methods, whereas D-VST uses its adaptive sampling strategy. As shown in Table TA0, D-VST is orders of magnitude faster than the other diffusion-baast methods and comparable to the GAN-based approach, highlighting its efficiency advantage for large WSI virtual staining over existing diffusion-based methods.
Table A10: Runtime comparison of Pix2pixHD generating WSI of 16.384x 16,384 pixels.
83 StainFuser
39 SynDiff [56), and D-VST for
Method
Pix2pixHD
StainFuser
SynDiff 172,032
D-VST (ours)
Time (second)
7,127
840,499
8,862
Influence of fine-tuning variational autoencoder (VAE). D-VST inherits the Stable Diffusion (SD) VAE (67] from PixArt-a (9. as it adopts the Diffusion Transformer (DiT) from PixArt-a as the denoising network. Our empirical evidence, both quantitative and qualitative, shows that D-VST with the SD VAE outperforms various GAN- and diffusion-based approaches on both virtual staining and downstream classification tasks. Thus, we argue that the SD VAE suffices for encoding and decoding of pathology images in D-VST, despite being pretrained on natural images and probably not being the optimal choice. We attribute this to SD VAE's strong encoding/decoding ability from large-scale, diverse training, and DiT's high compatibility with it. Notably, other works (e.g.. HistDiST (25). StainFuser (39]] also successfully used VAEs pretrained on natural images for histopathology virtual staining.
We also experimented with fine-tuning the SD VAE on the HE2IHC dataset, but observed mixed results: similar PSNR/SSIM but worse DISTS, FID, and KID. We visualize the reconstruction error distributions in Figure A8 The reconstructed images show blurred cell membranes in lesion regions. As shown in the MSE maps, these lesion regions exhibit the highest intensity, indicating that the reconstructed images deviate most from the ground truth in these areas. We speculate that this
A27

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Case
Case 2
Case
Case
Reconstruc THC
Real IHC
MSE Result
Figure A8: Reconstruct IHC denotes the generated images produced with the fine-tuned VAE, whereas Real IHC represents the corresponding ground-truth images. MSE Result refers to the pixel-wise mean squared error (MSE) maps between the reconstructed and real images, where regions with higher temperature indicate larger discrepancies. The red ellipses outline some areas where the reconstructed cell membranes in lesion regions appear noticeably blurred.
degradation is due to the limited training data, which prevented the VAE from learning pathological structures effectively; however, further validation with more data needed.
Impact of tone control image selection on the downstream task. To evaluate the robustness of D-VST to tone control images, we conduct an additional set of experiments in which only a single IHC conditioning patch is used. Specifically, we randomly select one IHC image from the sub-test set as the sole tone conditioning patch and repeat the experiment three times with different selections, reporting the averaged results in Table Otherwise, these experiments follow the same setting as Table 3 in the main text. As comparable to that obtained using multiple patches (originally reported T) This demonstrates that D DS''s downstream task performance is robust to the number of tone conditioning images, which we attribute to its effective disentanglement of tone and pathological conditions.
see, the performance with single IHC conditioning patch is
Table A11: Impact of tone control image selection on the downstream task.
No. tone patches
ACC
F1
Precision
Recall
Multiple (original)
0,9417 0.9415=t0.0068
0.9430 0.9427=t00004
0,9470
0.9388
Single (avg. over 3 runs)
0.9483-t0.0050
0.9416=0.0068
A28

---

Tane
IHC Result
Tone
IHC--Resall
Tome
IHC--Resih
GT
FEPE
Tonc
HE-Reat ResuRt
Tone 2
HE-R Resutc
Tonc 3
HE-Result
GT
Figure A9: Additional qualitative results for tone control. Top: HE to IHC; and bottom: FFPE to HE, The images are virtually stained the resolution 1024x 1024 pixels. GT: ground truth.
A29

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Case
Case
Case
Case
Case
Case 6
Case
HE
Peng et al.
PiX2PixHD
Pix2Pix
CycleGAN
SynDiff
GT
Figure A10: Qualitative comparison of HE to IHC virtual staining results by various methods that cannot control the tone the virtually stained images, including Peng et al. (61, Pix2PixHD (83). Pix2Pix (37), CycleGAN [100,, and SynDiff [56]. Note that the corresponding results by a few methods that control the tone of the re-stained images through image-based conditioning (including ours) are presented in Figure AIT for comparison. The images virtually stained at the resolution of 1024x 1024 pixels. GT: ground truth. By comparing the virtually stained images with the GT, we observe clear Type I (false positive, meaning hallucinated cancerous status) and Type II (false negative, meaning hallucinated cancer-fre status) errors for these methods.
A30

---

Case
Case
Case 5
Case
Case
StainFuser
Ours
Figure A11: Qualitative comparison with other methods for HE to IHC virtual staining with tone control, including StainFuser (39], baseline (training the same network in Figure 3 (left) D-VST but without the proposed two-stage condition-decoupling curriculum learning). The "Tone" row displays the tone-conditioning images. Note that the corresponding results by methods that cannot control the tone of the re-stained images are shown in Figure A10 for comparison. The images virtually stained the resolution of x024 1024 pixels. GT: ground truth. By comparing the virtually stained images with the GT, we can observe clear pathology leakages of Type (false positive, meaning hallucinated cancerous status) and Type II (false negative, meaning hallucinated cancer-free status) eπrors for the compared methods. In contrast, the results of our D-VST align closely with the pathological statuses and distributions of the GT, while accurately reflecting the tones of the conditioning images.
A31

---

HEFFPE
No-overiap
MulsDiduusoon
SpolDiiisson
Our
GT
Figure A12: Comparison of sampling strategies for zero-shot virtual staining of large histopathology images x 2048 pixels). The red ellipses outline regions where the mosaic artifacts are prominent (best viewed zoomed in). GT: Ground truth.
A32

---

IHC (HER2 0)
HE (HER2 0)
IHC (HER2 0)
Tone (HER2 1+)
Tone (HER2 0)
IHC (HER2 3+)
HE (HER2 3+)
IHC (HER2 3+)
Figure A13: Examples of altra-high-resolune (1 (16,384x 16,384 pixels) HE2IHC virtual staining. The central column shows the source-domain HE images, whereas the left and right columns show the virtually stained IHC images conditioned on two different tone-control images from the target domain. The top and bottom rows show two examples. HER2 scores: no cancerous lesion, and I+, 2t, and 3+: increasing severity of cancerous lesions.
A33

---

16000> 15096
4096x4096
FFPE
4096x4096
Tone 1
16000x15000 15096
4096x4096
HE
Tone
16000-15000 15096
4096
HE 2
4096x4096
Figure A14: Virtual staining of an FFPE WSI in EX--FFPE2HE, illustrated with two tone-conditioning images (Tone 1 and Tone 2) from the target domain. The virtual staining results corresponding to Tone and Tone 2 are HE and HE 2, respectively.
A34

---

40090=32496
FFPE
8192<8192
8192>8192
Tone 1
40490
HE
8192-8192
8192>8192
Tone 2
40490
8192<8192
8192>8192
Figure A15: Virtual staining of an FFPE WSI in EXt-FFPE2HE, illustrated with two tone-conditioning images (Tone I and Tone 2) from the target domain. The virtual staining results corresponding to Tone and Tone 2 are HE and HE 2, respectively.
A35


Reviews:
[{"review_id": "WDcaB8i1ms", "reviewer": "Reviewer_ckuD", "summary": "This paper presents a new method called D-VST. It is for cross-dye virtual staining of pathology images. The method uses a Diffusion Transformer. It tackles two main problems. One is \"pathology leakage\". This is when pathology features from a reference image wrongly appear in the result. The other problem is the \"mosaic artifact\". This happens when making very large Whole Slide Images (WSIs). D-VST uses two separate encoders for pathology and tone. It also uses a two-stage training plan. This helps to separate pathology from tone information. For large WSIs, the paper suggests a new patch sampling strategy. It focuses on low-frequency areas to avoid artifacts. This is also computationally efficient. The authors test their method on three tasks. The results show D-VST is better than other methods. It creates high-quality images and keeps the pathology correct. A classification task confirms this.", "questions": "NA", "limitations": "See the Weaknesses", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n1.\tD-VST is the first diffusion model to achieve tonal control for cross-staining virtual staining, filling a gap in existing methods.\n2.\tThe paper uses separate encoders to extract pathology structure and staining tone. A two-stage curriculum learning first focuses on pathology, then introduces tone control to prevent “pathology leakage.” A frequency-aware adaptive patch sampling enables efficient inference on gigapixel whole-slide images and removes mosaic artifacts.\n3.\tThe adaptive sampling method is a good innovation for large images. It solves the quality versus cost problem. This makes the tool practical.\n\n**Weaknesses**\n\n1.\tThe VAE used was pre-trained on a natural image (Stable Diffusion 1.5), which is not suitable for pathology images due to the obvious domain shift. You note in the appendix that the fine-tuning failed, but you do not analyze why, which makes the choice of VAE seem arbitrary. I suggest adding a direct performance comparison with pathology-specific VAEs (e.g., VAEs using the PathCLIP encoder) to quantify the impact of domain shifting. I would also suggest analyzing the distribution of reconstruction errors to show which specific pathology-specific structures are poorly reconstructed by current VAEs, rather than just reporting average scores.\n2.\tThe frequency-aware adaptive sampling is a key contribution, but the choice of hyperparameters α=1 and β=8 appears inadequately justified. The paper mentions an appendix for the grid search of n, but a similar rigorous ablation study for α (which controls the sampling probability distribution ) and β (which governs the cost-quality trade-off ) is absent. The robustness and generalizability of the sampling strategy depend heavily on these parameters, and a dedicated analysis is needed to strengthen this contribution.\n3.\tThe authors rightly note the difficulty in quantitatively evaluating billion-pixel WSIs due to the lack of suitable datasets. While understandable, this remains a limitation, especially since WSI processing is a core claim. You should suggest a way to test it. For example, you could measure consistency between patches. Or you could ask pathologists to rate the image quality blindly.\n4.\tThe paper lacks theoretical analysis or proof and relies solely on empirical evidence. This may limit a deeper understanding of model behavior and principles, especially when explaining why two-stage learning and frequency sampling are effective.\n5.\tIt is recommended to test the model on more diverse pathology datasets and staining types to assess its robustness and generalization capabilities. For example, images of different organs or pathology types could be included.\n6.\tOn Ext-FFPE2HE, lower PSNR/SSIM vs. task-specific [27] attributed to \"poor pixel alignment\" without evidence.\n7.\tThere is insufficient discussion of the direction of generation and the direction of medical treatment. It would have been better for the authors to present more methods [A-E].\n\n**Minor issues**\n\n1. Simplify long sentences for readability.\n2. Check figure/table numbering and ensure all subfigures have clear captions.\n3. Standardize reference formats.\n\n**Reference**\n\n[A] Improving generative adversarial network inversion via fine-tuning GAN encoders[J]. TPAMI, 2024.\n\n[B] Multi-marginal wasserstein gan[J]. Advances in Neural Information Processing Systems, 2019, 32. \n\n[C] Closed-loop matters: Dual regression networks for single image super-resolution[C]CVPR. 2020.\n\n[D] Cross-ray neural radiance fields for novel-view synthesis from unconstrained image collections[C] ICCV 2023\n\n[E] Segment anything model for medical image segmentation: Current applications and future directions[J]. Computers in Biology and Medicine, 2024", "was_revised": true, "final_justification": "Thanks for the authors' detailed response. They have addressed my concerns. I have raised my score.", "rebuttal": "> **W1**: The VAE used was pre-trained on a natural image (Stable Diffusion 1.5), which is not suitable for pathology images due to the obvious domain shift. You note in the appendix that the fine-tuning failed, but you do not analyze why, which makes the choice of VAE seem arbitrary. I suggest adding a direct performance comparison with pathology-specific VAEs (e.g., VAEs using the PathCLIP encoder) to quantify the impact of domain shifting. I would also suggest analyzing the distribution of reconstruction errors to show which specific pathology-specific structures are poorly reconstructed by current VAEs, rather than just reporting average scores.\n\n**A1**: We appreciate the reviewer's insightful comments.\n\nD-VST inherits the Stable Diffusion (SD) VAE [56] from PixArt-α [8], as it adopts the Diffusion Transformer (DiT) from PixArt-α as the denoising network.\n**Our empirical evidence, both quantitative and qualitative, shows that D-VST with the SD VAE outperforms various GAN- and diffusion-based approaches on both virtual staining and downstream classification tasks.**\nThus, we respectfully but firmly argue that the SD VAE suffices for encoding and decoding of pathology images *in D-VST*, despite being pretrained on natural images and probably not the optimal choice.\nWe attribute this to SD VAE’s strong encoding/decoding ability from large-scale, diverse training, and DiT's high compatibility with it.\nNotably, other works (e.g., HistDiST [F], StainFuser) have also successfully used VAEs pretrained on natural images for histopathology virtual staining.\n\nMeanwhile, we agree with the reviewer that further analysis is needed for insights beyond average scores.\nWe fine-tuned the SD VAE on our data but observed mixed results (Appendix Lines 91-93): similar PSNR/SSIM but worse DISTS, FID, and KID.\n**Following your suggestion, we have visualized the reconstruction error distributions: the reconstructed images showed blurred cell membranes in lesion regions.**\nWe will include these images and pixel-wise difference maps in the Appendix.\nWe speculate this degradation is due to limited training data that prevented the VAE from learning pathological structures well, but further validation with more data is needed.\n\nLastly, we expect that **pathology-specific VAEs could further improve D-VST's performance**.\nHowever, to our knowledge, no publicly available pathology-specific VAEs exist for now, and training such models (e.g., with PathCLIP encoder) is nontrivial and goes beyond the scope of this paper.\nAs suggested, we plan to study the impact of domain shift via direct comparison with pathology-specific VAEs in future work.\n\n[F] Großkopf, Erik, et al. \"HistDiST: Histopathological Diffusion-based Stain Transfer.\" arXiv preprint arXiv:2505.06793 (2025).\n\n> **W2**: The frequency-aware adaptive sampling is a key contribution, but the choice of hyperparameters α=1 and β=8 appears inadequately justified. ... The robustness and generalizability of the sampling strategy depend heavily on these parameters, and a dedicated analysis is needed to strengthen this contribution.\n\n**A2**: We agree that a dedicated analysis of α and β strengthens our contribution.\n**A rigorous ablation study with grid search was included in the Appendix (Lines 40-54, Figure A1)**, as mentioned in Lines 321-322 of the main text.\nOur analysis shows: (1) α $\\in$ {0.5, 1, 2} yields similar performance, with α=1 being optimal; (2) β=8 and 16 saturate performance, and we choose 8 for lower computational cost.\nWe believe this analysis justifies our choice of α and β.\n\n> **W3**: The authors rightly note the difficulty in quantitatively evaluating billion-pixel WSIs due to the lack of suitable datasets. ... You should suggest a way to test it. For example, ... Or you could ask pathologists to rate the image quality blindly.\n\n**A3**: Thank you for the constructive suggestions on evaluating WSIs. \n\nAccordingly, we have asked **two board-certified pathologists to blindly rank WSIs** virtually stained by our D-VST, a representative GAN-based method (pix2pixHD), and two diffusion-based methods (SynDiff, StainFuser), considering image quality and pathology correctness.\nThe evaluated WSIs include two HE2IHC and two FFPE2HE images, shown in Appendix Figures A6, A7, and A8, respectively.\n**The mean ranking is: D-VST (1.0), pix2pixHD (2.5), StainFuser(3.0), and SynDiff (3.5)**, demonstrating D-VST’s superior virtual staining quality.\n\n> **W4**: The paper lacks theoretical analysis or proof and relies solely on empirical evidence. This may limit a deeper understanding of model behavior and principles, especially when explaining why two-stage learning and frequency sampling are effective.\n\n**A4**: We appreciate the reviewer's insightful point.\n\nAs the reviewer noted in the **Strengths** comments, our main contributions are: 1) the first diffusion model for tonal control in cross-staining virtual staining, and 2) innovative adaptive sampling for efficient gigapixel WSI inference and mosaic artifact removal, which fill a gap in existing methods and make the tool practical.\nThus, **our paper presents significant methodological contributions**.\nIn addition, we provided **theoretical motivations and principles for our methodology design, supported by empirical evidence**.\n\nFor the **two-stage learning** strategy (Lines 147-152): \"... in Figure 2, **training with mingled pathology and tone controls often causes pathological status to leak through tone conditioning**, leading to incorrect staining outcomes. This occurs because **the model confuses the purposes of the conditioning images and mistakes the tone-conditioning image for the source of the pathology status**. Thus, **the key to effectively preventing pathology leakage is to decouple pathology and tone conditions by making the model learn both control signals precisely. For this purpose, we design a novel two-stage curriculum learning [4] scheme.**\"\nIn the first stage, the model learns to effectively extract and utilize the pathology information in the pathology-conditioning image.\nThen, in the second stage, the tone condition is introduced for more complex, joint learning of pathology and tone conditions.\n**This two-stage process enables effective decoupling of pathology and tone conditions**.\nOur ablation study (Table 4, w/o Curriculum) confirms that **the two-stage learning is indeed the key to pathology preservation**.\n\nFor the **frequency sampling** (Lines 192–196):\n\"..., we observe that **the mosaic artifact is more pronounced in low-frequency regions of the virtually stained images**. This occurs because, **unlike areas with complex textures that contain abundant clues guiding the virtual staining process, low-frequency regions require more overlapping patches for consistent denoising results**. To improve the generation quality of the low-frequency regions while simultaneously controlling computational overhead, we propose a frequency-aware adaptive patch sampling strategy (**to sample more patches in low-frequency regions**).\"\nThe empirical results (Appendix Lines 46-50) **validate our observation and design principle of sampling more in low-frequency regions**.\n\n> **W5**: It is recommended to test the model on more diverse pathology datasets and staining types to assess its robustness and generalization capabilities. For example, images of different organs or pathology types could be included.\n\n**A5**: We appreciate the reviewer's excellent point.\n\n**D-VST has been evaluated on datasets of two organs/cancer types**: breast cancer (HE2IHC, FFPE2HE) and colon cancer (HE2mIHC).\nIts consistent performance across cancer types demonstrates its robustness and generalization.\nWe will clarify this in the final manuscript and plan to extend to more diverse tasks in the future, as the reviewer recommended.\n\n> **W6**: On Ext-FFPE2HE, lower PSNR/SSIM vs. task-specific [27] attributed to \"poor pixel alignment\" without evidence.\n\n**A6**: Thank you for the valuable comment.\n\nIn Lines 252-253, we conjectured that D-VST's lower PSNR/SSIM versus [27] on FFPE2HE \"is because the **micro-level correspondence (pixel- and structure-wise)** between the paired images in this dataset is not as good as the other two.\"\nThis misalignment is inherent in all datasets used in our paper due to the consecutive slicing and chemical staining process, and we visually find it more serious in the FFPE2HE dataset.\nAfter reading this comment, we have resorted to the following procedures to **quantify the structural (mis)alignment between paired images**.\nWe apply the Canny edge detector and compute the **Hausdorff distance, intersection-over-union (IoU), and Dice similarity** between edge maps of the source and target images.\nIntuitively, lower Hausdorff distance and higher IoU and Dice metrics indicate better structural alignment.\nThe results on the HE2IHC and FFPE2HE datasets are:\n\n| Datasets | Hausdorff↓ | IoU↑ | Dice↑ |\n|-------|-------|-------|-------|\n| HE2IHC | **30.92±12.05** | **0.150±0.026** | **0.260±0.041** |\n| FFPE2HE | 36.63±33.13 | 0.138±0.011 | 0.242±0.016 |\n\n**FFPE2HE shows consistently worse alignment metrics, supporting our conjecture.**\nIn particular, the Hausdorff distance measures the maximum deviation between two point sets, highlighting the worst-case alignment errors.\nThus, **the substantially larger Hausdorff distances indicate more extreme misalignments.**\nAccordingly, we have also observed more poorly registered cases in FFPE2HE.\nWe will add these quantitative and qualitative evidences to the Appendix.\n\n> **W7**: There is insufficient discussion of the direction of generation and the direction of medical treatment. It would have been better for the authors to present more methods [A-E].\n\n**A7**: Thank you for pointing out these excellent works.\nWe will add a discussion of them to strengthen our paper.\n\n> **Minor issues**\n\n**A8**: Thank you for these suggestions. We will address all presentation issues."}, {"review_id": "4iJ0JVsnz4", "reviewer": "Reviewer_3v65", "summary": "The authors present a diffusion-based virtual staining (VS) model specifically designed to address potential tone variation and pathology leakage in the generated images. The three main contributions of the framework are: (1) diffusion-based staining and tone control, (2) a curriculum learning method to prevent pathology leakage, and (3) a new frequency domain content-based sampling technique to produce high-quality VS images.  The analysis includes comparisons against GAN-based methods, showing superior performance compared to methods in the literature while also decreasing the computational complexity", "questions": "Can the authors give some timing information for large WSI generation and compare it against at least StainFuse and SynDiff\n\nDid the authors observe any significant difference between the VS images when they used different tone domain image examples for the same sample? How robust is the model against variation of the tone domain images?", "limitations": "Performance comparisons are limited to FID, PSNR, SSIM metrics, which can be diagnostically deceptive.", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n- The authors presented a systematic approach to the problem. They tailored their solutions according to the needs of the application and drawbacks in the current methods. \n- The experiments are well designed and illustrate the performance of the model well. \n- Ablation studies are presented with illustrate the performance boost introduced by different components\n- Patch selection strategy is well justified, provide good efficacy\n\n\n\n**Weaknesses** \n\n- It is not clear why the authors particularly picked SynDiff and StainFuser for comparisons. There are several other methods available in the literature (such as \"Pixel super-resolved virtual staining of label-free tissue using diffusion models\" or DeepLiif - rather than cycleGAN)\n- Conclusions section is too short and does not contain anything more than the summary of the paper\n- It would have been nice to give a WSI example in the paper, and also some timing comparison between DVST and the competing methods for virtual staining of WSI images.", "was_revised": true, "final_justification": "The authors replied to all my comments. Unfortunately, I do not see the changes in the paper since the updated version is not available in the system. If the final version of the paper is updated as described in the authors' rebuttal, I have no further reviews or comments. I will keep my score", "rebuttal": "> **C1 (W1)**: It is not clear why the authors particularly picked SynDiff and StainFuser for comparisons. There are several other methods available in the literature (such as \"Pixel super-resolved virtual staining of label-free tissue using diffusion models\" (PSRVS) or DeepLiif - rather than cycleGAN)\n\n**A1**: Thank you for your comments.\n\nWe selected SynDiff and StainFuser as they are established diffusion-based models for medical image translation and histological style transfer, respectively, aligning well with our study’s focus. They represent the diffusion-based approaches we aim to compare against.\n\nWe also thank the reviewer for bringing two additional state-of-the-art approaches to virtual staining, one belonging to diffusion-based and the other to GAN-based models, to our attention.\nFollowing your advice, we have included PSRVS and DeepLiif (retrained per the official protocols) for comparison on the HE2IHC [51] dataset:\n| Method | DISTS↓ | FID↓ | KID↓ | PSNR↑ | SSIM↑ |\n|-------|-------|-------|-------|-------|-------|\n| D-VST (ours) | **0.154** | **33.16** | **0.0055** | **18.11±3.874** | 0.407±0.136 |\n| PSRVS | 0.422 | 230.3 | 0.2283 | 17.94±4.193 | 0.396±0.128 |\n| DeepLiiF | 0.305 | 140.3 | 0.1379 | 17.96±3.713 | **0.418±0.129** |\n\nWith the due caution that these two methods may not be fully optimized for this task, we find that **our D-VST substantially outperforms PSRVS and DeepLiif in DISTS, FID, and KID,** and is comparable in PSNR and SSIM.\nWe will include these results in the final version.\n\n> **C2 (W2)**: Conclusions section is too short and does not contain anything more than the summary of the paper\n\n**A2**: We appreciate the valuable feedback. \n\nIn response, we will substantially revise the Conclusion section to go beyond a summary, **highlighting the broader implications, potential impact, and future research directions of our work**. We believe these additions provide a more comprehensive and meaningful conclusion to the paper.\n\nAdditionally, we **supplemented the Conclusion section with a Limitations section in the Appendix**, where we\n- Discussed the multi-step denoising process inherent in diffusion models that hinders further efficiency improvement, and envisioned consistency models and distillation retraining as potential solutions;\n- Considered replacing the VAE pretrained on natural images with one fine-tuned or pretrained on histopathology images within our D-VST framework.\n\n> **C3 (W3)**: It would have been nice to give a WSI example in the paper, and also some timing comparison between DVST and the competing methods for virtual staining of WSI images.\n\n> **(Q1)**: Can the authors give some timing information for large WSI generation and compare it against at least StainFuse and SynDiff\n\n**A3**: Thank you for the constructive comments.\n\nFor **WSI visualization**, we **included examples of virtually stained WSIs** (two from the HE2IHC dataset and two from the FFPE2HE dataset) in Figures A6, A7, and A8 of the Appendix.\n\nFor timing, we have **recorded the generation times for the WSIs** in Figure 1 (16,384×16,384 pixels; HE to IHC) using a representative GAN-based method (pix2pixHD), two diffusion-based methods (StainFuser and SynDiff), and our D-VST.\nTo avoid the unwanted mosaic artifacts, we implement the MultiDiffusion [3] sampling strategy for the compared methods, while D-VST uses its adaptive sampling strategy.\nThe results are:\n| Method | Pix2pixHD | StainFuser | SynDiff | D-VST (ours) |\n|-------|-------|-------|-------|-------|\n| Time (second) | 7,127 | 840,499 | 172,032 | 8,862 |\n\nAs we can see, **D-VST is orders of magnitude faster than the other diffusion-based methods and comparable to the GAN-based approach**, highlighting its efficiency advantage for large WSI virtual staining over existing diffusion-based methods.\n\n> **C4 (Q2)**: Did the authors observe any significant difference between the VS images when they used different tone domain image examples for the same sample? How robust is the model against variation of the tone domain images?\n\n**A4**: We appreciate the reviewer's insightful questions.\n\nWe observed **significant differences in tones** in virtually stained images when using different tone conditioning images for the same source image.\nThis is evidenced by extensive qualitative results shown in Figure 5 (main text), as well as Figures A2, A6, A7, and A8 (Appendix), where **each source image stained with a few different tone images results in apparent tone variations**, demonstrating effective tone control of D-VST.\n\nAt the same time, these figures show that **the generated images consistently preserve the pathology of the source image, regardless of tone variation, thus fulfilling the pathology fidelity requirement of virtual staining**.\nFor example, in Figure A6 (Appendix), images generated with tone images of different HER2 scores (with and without cancerous lesions) maintain the same HER2 score as the source, despite distinct tones.\nThus, **D-VST is robust to tone domain variation in terms of pathology fidelity**.\n\n> **C5 (Limitations)**: Performance comparisons are limited to FID, PSNR, SSIM metrics, which can be diagnostically deceptive.\n\n**A5**: Thank you for this important point.\n\nWe agree that image quality metrics (e.g., FID, SSIM) alone may not reflect diagnostic value.\nTherefore, we included a **downstream pathology classification task** (Lines 268-283), involving four HER2 scores: HER2 0: no cancerous lesions, and 1+, 2+, and 3+: increasing severity of cancerous lesions, with higher scores indicating more pronounced lesions and more advanced disease stages.\nA classifier trained on real IHC images was tested on virtual IHC images stained from HE images.\nIf the virtual images are pathologically correct and realistic, classification performance should be similar to that on real ones.\n\nAs shown in Table 3, **the classifier performs similarly well on both real and D-VST-generated images, and D-VST outperforms other methods by large margins**.\nThis demonstrates that D-VST’s virtual images are of **high quality for potential diagnostic use**.\nNonetheless, we acknowledge the need for more diagnosis-oriented experiments on diverse tasks to further validate clinical usability in the future."}, {"review_id": "WbSLoxssUx", "reviewer": "Reviewer_843m", "summary": "The paper presents a diffusion transformer for virtual staining, e.g IHC staining from HE images, or HE staining from FFPE (unstained tissue). It has two main contributions: 1) it uses a two-stage curriculum learning scheme to separate tone and pathology features, solving the pathology leakage problem, and ii) it uses a frequency-aware adaptive patch sampling strategy to mitigate mosaic artefacts during high-resolution inference. They evaluate their model on three datasets and show improved or competitive performance compared to state-of-the-art virtual staining methods.", "questions": "1. According to Session: Datasets and Evalaution Metrics (line 218-265), authors trained three models, one for each dataset and evaluated the model on the testing split of the corresponding dataset. This already raised questions on the model generalisation. As for clinical usage, a model need to be evaluated on an independent patient cohort. \n2. Another question is for tone image selection. It is written that tone image is “ a random patch from the same WSI but not overlapping  with the target image for tone conditioning.” For model inference, do users need to select one tone image for the same WSI of a different stain? That leads to a practical problem, if users have the stained the same image with a different stain, why do they need the generated one? Is it possible to select the tone image from the training set. \n3. There is rapid development for pathology foundation model recently,  why not use pathology foundation models trained on multi stains as conditioning like UNI, https://huggingface.co/MahmoodLab/UNI2-h\n4. Ablation study on the autoencoder size would also be interesting, since authors only use a small and lightweight one.\n5. Ref [49] is a very relevant work as it does something similar but on TMAs and using GNN. Could authors discuss their methodological differences?", "limitations": "Maybe indeed authors can add a few sentence about the status of their model and potential limitations of using their model in clinical settings.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1: The paper is well written with clearly explained motivation, methodology and experimental design. \n\n2. The methodology is logical and well-designed. Although the essential concept like disentanglement of pathology features and tones, is not necessarily new (e.g. used in histology stain normalisation/augmentation), but the design of the presented model architecture is novel, as far as I can see. \n\n3. The experimental evaluation is also comprehensive, covering three different datasets. Particularly, the evaluation of downstream HER classification and scoring provides quantitative evidence of the proposed method over other methods in pathology leakage prevention (Table 3 back up qualitative results in Fig. 2) . \n\n4. Ablation studies show that two-stage curriculum learning is indeed the key of pathology structure preservation in terms of prediction accuracy of HER classification (from around 0.77 to 0.94).\n\nWeakness:\n1. The theoretical contribution of the paper is limited as it is pretty much a very applied paper (not necessarily a weakness of the paper itself, but theoretical contribution is something I like for NeurIPS conference)\n2. Sampling strategy seems only marginal better (according to Table 1). Is it worth the computational cost?\n3. Not entirely clear about model generalisation (see Questions for details)", "was_revised": true, "final_justification": "Authors have addressed my remaining concerns and I will keep my original accept decision.", "rebuttal": "> **C1 (W1)**: The theoretical contribution of the paper is limited as it is pretty much a very applied paper (not necessarily a weakness of the paper itself, but theoretical contribution is something I like for NeurIPS conference)\n\n**A1**: We thank the reviewer for **recognizing our significant methodological contributions**, including well-designed methodology, novel model architecture, and comprehensive experimental evaluation.\nThus, we respectfully but firmly argue that our focus on applied aspects does not diminish the impact of our work, especially considering **its high significance in the pathology AI sub-area of NeurIPS**.\nMeanwhile, we provided **theoretical motivations and principles for our methodology design, supported by empirical evidence**.\n\nMost notably, the **two-stage curriculum learning** is the key to pathology structure preservation, as the reviewer noted.\nIts theoretical motivation is detailed in Lines 147-152: \"... in Figure 2, **training with mingled pathology and tone controls often causes pathological status to leak through tone conditioning**, ... because **the model confuses the purposes of the conditioning images and mistakes the tone-conditioning image for the source of the pathology status**. Thus, **the key to effectively preventing pathology leakage is to decouple pathology and tone conditions by making the model learn both control signals precisely. For this purpose, we design a novel two-stage curriculum learning [4] scheme.**\"\nIn the first stage, the model learns to effectively extract and utilize the pathology information in the pathology-conditioning image.\nThen, in the second stage, the tone condition is introduced for more complex, joint learning of pathology and tone conditions.\n**This process enables effective decoupling of pathology and tone conditions**.\nOur ablation study in Table 4 shows that **removing the two-stage learning (w/o Curriculum) leads to the largest performance drops** in 3 of the 4 classification metrics, confirming our motivation and design principle.\n\n> **C2 (W2)**: Sampling strategy seems only marginal better (according to Table 1). Is it worth the computational cost?\n\n**A2**: Thank you for the insightful comment.\n\nWe believe the reviewer referred to Table 2, which compares sampling strategies.\n\nCompared with no-overlap sliding windows, our strategy increases computational cost with similar PSNR, SSIM, and DISTS.\nHowever, it **substantially improves the perceptual metrics (FID and KID)**, e.g., FID on HE2IHC improves from 37.748 to 33.162.\nIn addition, **no-overlap exhibits notable mosaic artifacts (Figure 6 and Appendix Figure A5), which cannot be effectively captured by the standard metrics but are visually significant**.\nIn contrast, **our strategy eliminates these artifacts visually**.\nHence, the quantitative and qualitative improvements justify the computational cost.\n\nCompared to MultiDiffusion [3], our strategy **achieves comparable (if not better) performance with only 1/8 computational cost, offering a worthy balance between quality and computational efficiency**.\n\n> **C3 (W3)**: Not entirely clear about model generalisation (see Questions for details)\n\n> **Q1**: According to Session: Datasets and Evalaution Metrics (line 218-265), authors trained three models, one for each dataset and evaluated the model on the testing split of the corresponding dataset. This already raised questions on the model generalisation. As for clinical usage, a model need to be evaluated on an independent patient cohort.\n\n**A3**: We appreciate the reviewer's insightful comments.\n\nThe three datasets involve distinct source (HE/FFPE) and target domains (HE/IHC/mIHC), causing significant challenges to training a unified model for all three tasks.\nThus, we followed most existing literature in training a separate model for each task, for agile method development and effective proof of concept, but plan to explore unified models in future work.\n\nMeanwhile, we agree that for clinical usage, a model needs to be evaluated on independent patient cohorts.\nWe additionally validated our D-VST on two external validation datasets of HE2IHC and FFPE2HE, i.e., **the same tasks but different patient cohorts** (Appendix Lines 59-68).\nNotably, we directly evaluated the models on the external datasets without further tuning, to assess generalization.\nAppendix Table A3 shows that **D-VST again achieves the best performance for the perception-oriented metrics (DISTS, FID, and KID) on both external datasets**, and competitive performance for PSNR and SSIM.\nThese results are consistent with those in the main text, demonstrating D-VST's **strong generalizability across independent patient cohorts**.\n\n> **C4 (Q2)**: Another question is for tone image selection. It is written that tone image is “a random patch from the same WSI but not overlapping with the target image for tone conditioning.” For model inference, do users need to select one tone image for the same WSI of a different stain? That leads to a practical problem, if users have the stained the same image with a different stain, why do they need the generated one? Is it possible to select the tone image from the training set.\n\n**A4**: Thank  you for the insightful questions.\n\nWe would like to clarify that, for model inference, **any image from the target domain with the desired tone can be used**.\nIn our experimental scenarios, the images having tones closest to the target image happen to be the patches from the same WSI.\nTherefore, we selected a random patch that does not overlap with the target image (to prevent ground truth information leak) to provide the desired tone, but this is not a requirement for practical use.\nAlso, it is **feasible to select the tone image from the training set**.\nIntuitively, selecting images with tones closer to the target images would lead to better evaluation metrics.\n\n> **C5 (Q3)**: There is rapid development for pathology foundation model recently, why not use pathology foundation models trained on multi stains as conditioning like UNI\n\n**A5**: We appreciate the reviewer's constructive comment.\n\nAccordingly, we have experimented with replacing OpenAI-CLIP [52] with UNI in our D-VST.\n| Model | DISTS↓ | FID↓ | KID↓ | PSNR↑ | SSIM↑ |\n|-------|-------|-------|-------|-------|-------|\n| OpenAI-CLIP | 0.154 | 33.16 | 0.0055 | 18.11±3.874 | 0.407±0.136 |\n| UNI | 0.157 | 33.13 | 0.0048 | 18.03±3.842 | 0.404±0.130 |\n\nThe above results on the HE2IHC dataset [51] show that **the performance is comparable between OpenAI-CLIP and UNI**.\nWe believe that while UNI offers strong pathological feature extraction, OpenAI-CLIP suffices for capturing tone information in D-VST.\nHence, **pathology foundation models trained on multi-stains like UNI are also excellent choices** for the proposed D-VST framework.\n\n> **C6 (Q4)**: Ablation study on the autoencoder size would also be interesting, since authors only use a small and lightweight one.\n\n**A6**: We thank the reviewer for the insightful comment.\n\nD-VST inherits the Stable Diffusion (SD) variational autoencoder (VAE) [56] from PixArt-α [8], as it adopts the Diffusion Transformer (DiT) from PixArt-α as the denoising network.\n**Our empirical evidence, both quantitative and qualitative**, shows that D-VST with the SD VAE outperforms various GAN- and diffusion-based approaches on both virtual staining and downstream classification tasks.\nThis **suggests that the SD VAE suffices for encoding and decoding of pathology images *in D-VST*, despite being lightweight**.\nWe attribute this to SD VAE’s strong encoding/decoding ability from large-scale, diverse training, and DiT's high compatibility with it.\n\nHowever, we agree with the reviewer that it is interesting to explore if enhancing the VAE, e.g., increasing its size, would bring further performance improvement.\nIn a related attempt (Appendix Lines 91-95), we fine-tuned the SD VAE on our experimental data but observed mixed results (similar PSNR and SSIM with poorer DISTS, FID, and KID).\nWe conjecture this might be due to the limited training data that prevented the VAE from learning pathological structures well, but further validation with more data is needed.\nIn future work, **we plan to explore larger VAEs following the reviewer's suggestion**.\n\n> **C7 (Q5)**: Ref [49] is a very relevant work as it does something similar but on TMAs and using GNN. Could authors discuss their methodological differences?\n\n**A7**: We appreciate the reviewer's excellent suggestion.\n\nKey methodological differences include:\n\n- [49] uses GANs, while D-VST uses diffusion models;\n- [49] does not require paired data for training; D-VST does (further discussed in our response to the following comment);\n- [49] has not addressed the mosaic artifacts (called \"tiling artefacts\" in [49]) among individually generated patches, whereas D-VST introduces adaptive patch sampling and overlapping fusion for effective removal;\n- [49] uses multi-scale consistency objectives (spatial); D-VST employs a dual-stage learning strategy (temporal).\n\n> **C8 (Limitations)**: Maybe indeed authors can add a few sentence about the status of their model and potential limitations of using their model in clinical settings.\n\n**A8**: Thank you for your valuable suggestion.\n\nObtaining paired cross-stain training data can be challenging and costly in real-world workflows, which may limit the scalability and applicability of D-VST.\nWhile such data can improve virtual staining performance with paired correspondence, unpaired data is substantially more scalable due to orders of magnitude larger amounts.\nIn future work, we plan to **explore benefiting from both the scalability of unpaired data and the quality of paired data via a combination of D-VST and approaches like CycleGAN-Turbo [A], which enable diffusion models to learn from unpaired data.**\n\n[A] Parmar, Gaurav, et al. \"One-step image translation with text-to-image models.\" (2024)."}, {"review_id": "a6xqyPCam0", "reviewer": "Reviewer_yaZj", "summary": "This work proposes a method for integrating tone control for virtual staining using diffusion transformer models. This integration is done to reduce false virtual staining of regions in tissue images (e.g., staining of non-cancer regions as cancer regions). The method is evaluated on several datasets and virtual staining tasks.", "questions": "Page 7 (lines 224-225): “use a random patch from the same WSI but not overlapping with the target image for tone conditioning”. Per this sentence, it sounds like a separate patch is selected for a source image. Is this realistic? In a real setting, there may not be more than one target image (e.g., an IHC image in case of HE-to-IHC image generation). Clarification is needed for how tone conditioning patches are selected during the training and test phases in the experiments.\n\nPage 8 (lines 274-276): “Next, for each HE image in the sub-test set, we randomly select an IHC image in the sub-test set that is not paired with the specific HE image as the tone condition for virtual staining.” Selecting a different tone condition image for each HE image may affect the experimental results. In a real application setting, there may not be multiple IHC images. Please clarify if multiple IHC patches were selected for tone conditioning in the experiments. If so, a set of experiments should be executed in which only one IHC conditioning patch is selected.", "limitations": ".", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The use of tone control to improve virtual staining performance is different and interesting. The proposed approach implements a two-stage training strategy to incorporate pathology and tone information. It also employs an adaptive patch-sampling technique to improve image generation quality in whole slide images. \n\nExperimental evaluation using several datasets and virtual staining tasks (HE-to-IHC, FFPE-to-HE and HE-to-mIHC) shows performance improvements by the proposed method in several metrics (FID, KID, SSIM, etc) compared with other methods. \n\nThe experimental evaluation also includes a downstream image classification task. This is a useful experiment as it shows the performance improvements are not just in image generation metrics (KID, FID, etc), but also have discernable impact in pathology focused tasks.", "was_revised": true, "final_justification": "The authors have provided clarifications to my questions.", "rebuttal": "> **Q1**: Page 7 (lines 224-225): “use a random patch from the same WSI but not overlapping with the target image for tone conditioning”. Per this sentence, it sounds like a separate patch is selected for a source image. Is this realistic? In a real setting, there may not be more than one target image (e.g., an IHC image in case of HE-to-IHC image generation). Clarification is needed for how tone conditioning patches are selected during the training and test phases in the experiments.\n\n**A1**: We appreciate the reviewer's insightful comments.\n\nTo clarify, a separate patch is indeed selected for each source image.\n**For our D-VST, the tone image can be any image from the target domain with the desired tone**.\nIn our experimental scenarios, the images having tones closest to the target image happen to be the patches from the same WSI.\nTherefore, we selected a random patch that does not overlap with the target image (to prevent ground truth information leak) to provide the desired tone, but this is not a requirement for practical use.\nThis setting is consistently used for model training and testing for results in Table 1, as described in Lines 175-178 and 224-226.\n\nMeanwhile, we acknowledge the reviewer's concern that there may not be more than one target-domain image in practice, forbidding the use of a separate tone conditioning image for each source image.\nIn such cases, **D-VST can use the single available target-domain image for tone control, resulting in virtually stained images with tones similar to that image and correct pathology**.\nWe have conducted additional experiments using one tone-conditioning image for all source images (see response to Q2).\n**The results show that D-VST remains effective with only one tone conditioning image and is not sensitive to the number of tone conditioning images.**\n\n> **Q2**: Page 8 (lines 274-276): “Next, for each HE image in the sub-test set, we randomly select an IHC image in the sub-test set that is not paired with the specific HE image as the tone condition for virtual staining.” Selecting a different tone condition image for each HE image may affect the experimental results. In a real application setting, there may not be multiple IHC images. Please clarify if multiple IHC patches were selected for tone conditioning in the experiments. If so, a set of experiments should be executed in which only one IHC conditioning patch is selected.\n\n**A2**: Thank you for the valuable suggestion.\n\nAbove all, we clarify that multiple IHC patches were used for tone conditioning in these experiments, as our primary target was to demonstrate that our D-VST can achieve pathology-leakage-free virtual staining with precise tone control.\nTherefore, selecting an IHC image that is different from the real one for each HE image already suffices for the experiments, without the need for constraining the total number of selected ones.\n\nHowever, we acknowledge the reviewer's concern that in a real application setting, there may not be multiple IHC images for tone control.\nTo address this concern, we have **conducted a set of additional experiments in which only one IHC conditioning patch is selected**.\nSpecifically, we randomly select an IHC image from the sub-test set as the only tone conditioning patch and repeat the experiment three times with different images selected, reporting the average results below:\n| No. tone patches | ACC | F1 | Precision | Recall |\n| ------ | ------ | ------ | ------ | ------ |\n| Multiple (original) | 0.9417 | 0.9430 | 0.9470 | 0.9388 |\n| Single (avg. over 3 runs) | 0.9415±0.0068 | 0.9427±0.0064 | 0.9483±0.0050 | 0.9416±0.0068 |\n\nAs shown, **the performance with a single IHC conditioning patch is similar to that with multiple patches** (originally reported in our paper).\nThis demonstrates that D-VST's performance on the downstream task is robust to the number of tone conditioning images, which we attribute to its effective decoupling of tone and pathology conditions."}, {"review_id": "DERZ7mWekB", "reviewer": "Reviewer_XhuF", "summary": "This paper presents D-VST (Diffusion Virtual Staining Transformer), a new diffusion-based method for virtual staining of histopathology images, with a focus on accurate pathology preservation and flexible tone control. Traditional methods often suffer from pathology leakage, where incorrect staining occurs due to entanglement of pathology and tone information. To address this, the authors propose:\n\n1. A dual-encoder architecture (pathology and tone encoders) that separates content and style in the staining process.\n2. A two-stage curriculum learning strategy to effectively train the model on these disentangled conditions.\n3. A frequency-aware adaptive patch sampling technique for efficient and scalable inference on ultra-high-resolution whole slide images, enabling zero-shot generalization.\n\nThe method demonstrates strong performance across three stain translation tasks, outperforming existing GAN-based and diffusion-based approaches in both visual quality and pathological fidelity.", "questions": "1. Clarification and Supporting Evidence for Figure 1 Claims\n\nFigure 1 highlights several core advantages of D-VST, such as being “Fast,” “Pathology-Correct,” and free of “Mosaic Artifacts.” However, these claims are not directly supported within the figure.\n\nSuggestion: Please include visual examples (e.g., zoom-in patches with and without mosaic artifacts) and/or quantitative metrics that demonstrate the absence of artifacts and the correctness of pathology preservation. While including some visual or metric-based evidence in Figure 1 would be ideal, alternatively, you could explicitly reference other figures, tables, or sections in the paper that substantiate these claims. For example, show where “no mosaic artifact” is evidenced, or where “fast” is quantified.\n\nEvaluation impact: Providing this evidence would significantly improve the clarity and credibility of the figure, and would strengthen the justification for your claims.\n\n2. Runtime and Efficiency Comparison Missing\n\nThe method is described as fast, but no runtime or efficiency comparisons are provided.\n\nSuggestion: Please provide a runtime or inference time comparison with baseline models (e.g., GAN-based and other diffusion-based methods), ideally on WSIs and/or standard image sizes.\n\nEvaluation impact: Demonstrating competitive or superior efficiency would support real-world deployment claims and positively influence the significance of the work.", "limitations": "No. The authors have addressed several important limitations. To further strengthen the discussion, it would be helpful to acknowledge the reliance on cross-dye paired training data, which may be challenging to obtain in practice. Highlighting this would enhance the clarity and practical relevance of the work.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. First Diffusion-Based Model with Tone Control for Cross-Dye Staining\n\nThe proposed Diffusion Virtual Staining Transformer (D-VST) is the first diffusion-based model to support versatile tone control in cross-dye virtual staining, marking a meaningful advance in the virtual staining literature.\n\n2. Pathology and Tone Representations Decoupling via Curriculum Learning\n\nD-VST introduces a two-stage curriculum learning strategy to disentangle pathology and tone representations, effectively addressing the critical issue of pathology leakage, where pathological and non-pathological regions are misrepresented during virtual staining.\n\n3. Frequency-Aware Adaptive Sampling for High-Quality Zero-Shot Virtual Staining for WSIs\n\nTo mitigate mosaic artifacts and enhance zero-shot generation quality on whole slide images (WSIs), the authors introduce a frequency-aware adaptive patch sampling strategy. By prioritizing sampling in low-frequency regions, where artifacts are more likely, this method enables efficient and high-fidelity inference on billion-pixel WSIs.\n\n4. Comprehensive Experimental Evaluation\n\nThe method is validated on three cross-dye staining tasks involving four stain types, with additional downstream classification tasks and ablation studies, demonstrating strong performance against recent approaches.\n\nWeaknesses:\n\n1. Insufficient Explanation of HER2 Labels in Figure 1\n\nFigure 1 includes references to HER2 0, 1+, 2+, 3+, but lacks explanation of what these labels mean clinically. This omission reduces clarity for readers unfamiliar with HER2 scoring systems.\n\n2. Evidence for Key Claims Should Be Included in Figure 1\n\nThe caption of Figure 1 claims the method is \"Fast\", \"Pathology-Correct\", and “No Mosaic Artifacts”, but no visual or quantitative evidence is provided to substantiate these assertions in this figure.\n\n3. Missing Runtime Comparison\n\nWhile the paper asserts that the method is computationally efficient, there is no runtime or inference speed comparison with other models.\n\n4. Requirement for Cross-Stain Paired Training Data\n\nD-VST relies on paired cross-stain training data, which can be challenging and costly to obtain in real-world medical imaging workflows. This reliance limits the method’s scalability and broader applicability, yet the paper does not discuss this practical constraint or propose solutions for unpaired or weakly supervised settings.", "was_revised": true, "final_justification": "Thanks to the authors for their comprehensive response, which resolved my concerns. I have accordingly increased my score.", "rebuttal": "> **C1 (W1)**: Insufficient Explanation of HER2 Labels in Figure 1: Figure 1 includes references to HER2 0, 1+, 2+, 3+, but lacks explanation of what these labels mean clinically. This omission reduces clarity for readers unfamiliar with HER2 scoring systems.\n\n**A1**: We appreciate the reviewer's valuable comment.\n\nTo improve clarity for readers unfamiliar with HER2 scoring systems, we will **add an explanation of the HER2 scores in the caption of Figure 1**: 0 indicates no cancerous lesion, while 1+, 2+, and 3+ represent increasing severity of cancerous lesions.\n\n> **C2 (W2)**: Evidence for Key Claims Should Be Included in Figure 1: The caption of Figure 1 claims the method is \"Fast\", \"Pathology-Correct\", and “No Mosaic Artifacts”, but no visual or quantitative evidence is provided to substantiate these assertions in this figure.\n> \n> **(Q1)**: Clarification and Supporting Evidence for Figure 1 Claims: Figure 1 highlights several core advantages of D-VST, such as being “Fast,” “Pathology-Correct,” and free of “Mosaic Artifacts.” However, these claims are not directly supported within the figure.\n>\n> Suggestion: Please include visual examples (e.g., zoom-in patches with and without mosaic artifacts) and/or quantitative metrics that demonstrate the absence of artifacts and the correctness of pathology preservation. While including some visual or metric-based evidence in Figure 1 would be ideal, alternatively, you could explicitly reference other figures, tables, or sections in the paper that substantiate these claims. For example, show where “no mosaic artifact” is evidenced, or where “fast” is quantified.\n>\n> Evaluation impact: Providing this evidence would significantly improve the clarity and credibility of the figure, and would strengthen the justification for your claims.\n\n**A2**: We thank the reviewer for the detailed, helpful suggestions.\n\nTo address these points, we will revise Figure 1 as follows:\n\n- **Fast**: We will reference the inference time comparison table (see our response to the following comment), which shows that our D-VST re-stains the large WSI image in Figure 1 orders of magnitude faster than representative diffusion-based approaches.\n- **No Mosaic Artifacts**: We will add zoomed-in patch views to demonstrate the absence of mosaic artifacts. Also, we will refer to Figure 6 and Figure A5 (Appendix), which contrast the mosaic-artifact-free results by D-VST with those with noticeable mosaic artifacts by the No-overlap method.\n- **Pathology-Correct**: The zoomed-in patch views will also illustrate pathology correctness. Additionally, we will reference 1) Figures A3 and A4 (Appendix), which qualitatively contrast our pathology-correct virtual staining results with results by other methods; and 2) Table 3, which quantitatively compares the pathology accuracy of virtually stained images by various approaches, with a downstream pathology image classification task. Both the qualitative and quantitative results show that our method outperforms the compared ones.\n\nWe believe these changes will make Figure 1 clearer and better supported, strengthening the justification for our claims.\n\n> **C3 (W3)**: Missing Runtime Comparison: While the paper asserts that the method is computationally efficient, there is no runtime or inference speed comparison with other models.\n\n> **(Q2)**: Runtime and Efficiency Comparison Missing: The method is described as fast, but no runtime or efficiency comparisons are provided.\n>\n> Suggestion: Please provide a runtime or inference time comparison with baseline models (e.g., GAN-based and other diffusion-based methods), ideally on WSIs and/or standard image sizes.\n>\n> Evaluation impact: Demonstrating competitive or superior efficiency would support real-world deployment claims and positively influence the significance of the work.\n\n**A3**: Thanks for the constructive suggestion.\n\nAccordingly, we have recorded the generation times for the WSIs in Figure 1 (16,384×16,384 pixels; HE to IHC) using a representative GAN-based method (pix2pixHD), two diffusion-based methods (StainFuser and SynDiff), and our D-VST.\nTo avoid the unwanted mosaic artifacts, we implement the MultiDiffusion [3] sampling strategy for the compared methods, while D-VST uses its adaptive sampling strategy.\nThe results are:\n| Method | Pix2pixHD | StainFuser | SynDiff | D-VST |\n|-------|-------|-------|-------|-------|\n| Time (second) | 7,127 | 840,499 | 172,032 | 8,862 |\n\nAs we can see, **our D-VST is orders of magnitude faster than the other diffusion-based methods and comparable to the GAN-based approach**, highlighting its efficiency advantage for large WSI virtual staining over existing diffusion-based methods.\n\n> **C4 (W4)**: Requirement for Cross-Stain Paired Training Data: D-VST relies on paired cross-stain training data, which can be challenging and costly to obtain in real-world medical imaging workflows. This reliance limits the method’s scalability and broader applicability, yet the paper does not discuss this practical constraint or propose solutions for unpaired or weakly supervised settings.\n> \n> **(Limitations)**: No. The authors have addressed several important limitations. To further strengthen the discussion, it would be helpful to acknowledge the reliance on cross-dye paired training data, which may be challenging to obtain in practice. Highlighting this would enhance the clarity and practical relevance of the work.\n\n**A4**: We thank the reviewer for acknowledging our efforts in addressing several important limitations, and more importantly, for pointing out another limitation that should be discussed.\n\nWe acknowledge that obtaining paired cross-stain training data can be challenging and costly in real-world workflows, which may limit the scalability and applicability of D-VST.\nWhile such data can improve virtual staining performance by paired correspondence, unpaired data is substantially more scalable due to orders of magnitude larger amounts.\nIn future work, we plan to **explore benefiting from both the scalability of unpaired data and the quality of paired data via a combination of D-VST and approaches like CycleGAN-Turbo [A], which enable diffusion models to learn from unpaired data.**\nWe will add this discussion to the final manuscript.\n\n[A] Parmar, Gaurav, et al. \"One-step image translation with text-to-image models.\" arXiv preprint arXiv:2403.12036 (2024)."}]

Author rebuttal:

Meta-review: This paper presents D-VST (Diffusion Virtual Staining Transformer), a novel framework for pathology-correct, tone-controllable cross-dye virtual staining of whole slide images (WSIs). The core contributions include: 1) a dual-encoder architecture (pathology and tone encoders) combined with a two-stage curriculum learning scheme to decouple pathology and tone information, addressing "pathology leakage"; 2) a frequency-aware adaptive patch sampling strategy for efficient, high-quality inference on ultra-high-resolution WSIs, mitigating mosaic artifacts; and 3) demonstrating superior performance across three virtual staining tasks compared to state-of-the-art GAN and diffusion-based methods.

Reviewers raised concerns about VAE domain shift, hyperparameter justification, WSI evaluation metrics, theoretical analysis, and generalizability. During the rebuttal, the authors responded comprehensively and the reviewers are convinced by the authors' reponses, though theoretical gaps and paired data limitations remain to be addressed in future work.

In summary, D-VST makes impactful advancements in virtual staining, with practical implications for computational pathology. The paper is worthy of acceptance, with the expectation that the authors address the identified weaknesses in the final version (e.g., expanding theoretical motivations and discussing paired data limitations more explicitly).
Decision: Accept (poster)