Title: SGCD: Stain-Guided CycleDiffusion for Unsupervised Domain Adaptation of Histopathology Image Classification
Venue: NeurIPS 2025
Accepted: yes
Keywords: Transfer Learning

Domain Adaptation

Generative Models
Abstract: The effectiveness of domain translation in addressing image-based problems of Unsupervised Domain Adaptation (UDA) depends on the quality of the translated images and the preservation of crucial discriminative features. However, achieving high-quality and stable translations typically requires paired data, which poses a challenge in scenarios with limited annotations in the target domain. To address this issue, this paper proposes a novel method termed Stain-Guided Cycle Diffusion (SGCD), employing a dual diffusion model with bidirectional generative constraints to synthesize highly realistic data for downstream task fine-tuning. The bidirectional generative constraints ensure that the translated images retain the features critical to the downstream model in properly controlling the generation process. Additionally, a stain-guided consistency loss is introduced to enhance the denoising capability of the dual diffusion model, thereby improving the quality of images translated between different domains using latents from one domain and a diffusion model trained on another. Experiments conducted on four public datasets demonstrate that SGCD can effectively enhance the performance of downstream task models on the target domain.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a novel method called Stain-Guided CycleDiffusion (SGCD) for unsupervised domain adaptation in histopathology image classification. Recognizing that conventional models struggle to generalize across hospitals due to differences in staining protocols and imaging devices, the authors introduce a dual diffusion model framework that enables style transfer between domains without requiring paired data. The method leverages a cyclic structure and introduces a stain-guided consistency loss to preserve crucial structural and semantic information during the domain translation process. Experimental results on multiple public pathology datasets demonstrate that SGCD outperforms existing stain normalization, GAN-based, and general domain adaptation approaches, particularly in maintaining high-quality, diagnostically relevant features in the target domain images. The approach is designed to facilitate more robust histopathology image analysis across diverse clinical environments.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- Originality:\nThe paper presents a timely and creative approach by adapting diffusion models—rather than traditional GANs—to the problem of unsupervised domain adaptation in histopathology images. The integration of a cyclic framework with stain-guided consistency loss offers a fresh perspective within this area.\n\n- Significance:\nDomain shift remains a major barrier for clinical deployment of machine learning in pathology. By addressing domain adaptation without requiring paired data or precise reference images, the proposed method has practical relevance for real-world applications.\n\nWeaknesses\n- Clinical Relevance and Semantic Consistency:\nWhile the paper achieves strong quantitative results in classification tasks, it remains unclear whether the transformed images consistently preserve diagnostically critical structures at a level required for clinical decision-making. The stain-guided consistency loss primarily enforces color and statistical similarity, but does not guarantee preservation of fine-grained semantic or pathological features—an aspect that is crucial in actual pathology workflows.\n\n- Rare Cohort and Limited Data Scenarios:\nThe method, like many domain adaptation approaches, relies on sufficient representation of target domain characteristics in the training data. In practice, rare disease subtypes or underrepresented cohorts remain a significant challenge. The current evaluation does not directly address how well the model preserves or generalizes rare pathological features, which can limit its impact in real-world clinical settings.\n\n- Practical Deployment Considerations:\nAlthough diffusion models offer benefits in diversity and stability over GANs, they are also considerably more resource-intensive, both in terms of training and inference time. The paper does not thoroughly discuss these limitations or provide guidance for efficient deployment in clinical environments.\n\n- Ablation and Qualitative Assessment:\nWhile the proposed method demonstrates quantitative superiority, a more detailed ablation study dissecting the contributions of each architectural component (e.g., cyclic structure, stain-guided loss) would strengthen the claims. Additionally, qualitative analysis—such as expert review of the generated images for diagnostic fidelity—would be valuable for establishing trust in the method.\n\nQUESTIONS:\n1. Clinical/Pathological Semantic Consistency:\nCan the authors provide more evidence that the proposed method preserves diagnostically meaningful structures in the transformed images? For example, have any pathology experts reviewed the generated images for semantic fidelity, or are there metrics (e.g., segmentation overlap, class activation consistency) that can better quantify this? Additional qualitative or expert-blinded assessments would be helpful to build clinical trust in the transformations.\n\n2. Handling of Rare Cohorts and Limited Data:\nHow does the method perform when the target domain contains rare disease subtypes or underrepresented pathological features? Are there experiments or analyses—such as leave-one-class-out, few-shot adaptation, or stratified performance reporting—that can clarify the model’s robustness in these challenging settings? If possible, consider including experiments or discussions on rare cohort generalization.\n\n3. Component-wise Ablation and Contribution Analysis:\nCould the authors provide a more detailed ablation study to isolate the effect of each architectural component, such as the cyclic structure and the stain-guided consistency loss? Understanding the individual contributions would help clarify which elements are most important for the observed gains.\n\n4. Practical Deployment and Computational Overhead:\nGiven the resource demands of diffusion models, what are the practical implications for clinical deployment, especially compared to existing CNN or GAN-based approaches? Are there strategies to mitigate computational costs, or are there scenarios where the overhead is justified by performance gains?\n\n5. Failure Cases and Limitations:\nCan the authors share any observed failure cases or known limitations—such as when the domain shift is too large, or when key features are not preserved? Transparency about boundaries of the method’s effectiveness will be valuable for both clinical and research users.\n\n\nScore Adjustment Criteria:\nIf the authors can provide convincing evidence of preserved diagnostic structures (via expert review or strong semantic metrics), demonstrate robustness to rare or limited data scenarios, and clarify the practical deployment implications, my evaluation score would increase. Conversely, persistent uncertainty in these areas would limit my confidence in the method’s clinical applicability.\n\nLIMITATIONS:\nThe authors have addressed several methodological limitations, such as the need for sufficient target domain diversity and the computational demands of diffusion models. However, the discussion of clinical limitations—especially regarding semantic consistency of diagnostically relevant features and handling of rare disease cohorts—could be expanded. I would encourage the authors to include more discussion of these aspects, as well as to consider the potential risks of over-reliance on synthetic images in clinical decision-making. Explicit acknowledgment of these points, and transparency about where the method may or may not be reliable, would further strengthen the paper.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-19"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a new technique for unsupervised domain adaptation that uses diffusion models. The method is based on converting source domain images in the target style domain using a bidirectional diffusion process. The new source images obtained are used to train a target classifier by using cross entropy on those images and aligning distribution between target and source.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n1. The paper is well written and easy to follow to understand the proposed architecture. \n\n2. The proposed SGCD method uses a bidirectional diffusion framework combined with a stain-guided consistency mechanism. Unlike traditional domain adaptation approaches based on unidirectional image translation  (GANs), SGCD employs two separate diffusion models. Each model is trained on the source and target domains, respectively, enabling a cyclic conversion process (S → T → S and T → S → T)\n. This dual-path architecture ensures that the generated images retain essential structural and semantic features of the original data. Furthermore, the Stain-Guided Consistency loss guides the reverse process during generation, allowing more consistency for style transfer across domains.\n \n3. Also, the proposed approach doesn’t require paired images across domains, which is a significant advantage in medical imaging scenarios such as histology.\n\n4. Empirical results show improved performance on different standard histology datasets.\n\n**Weaknessess:**\n\n\n\n1. The second part of the method relies on a standard feature alignment strategy that is already used in existing domain adaptation literature [1]. \n\n2. Since the adaptation step is not novel, the main contribution lies in the generation part of the source images in the style of the target domain using diffusion models. However, the paper does not provide sufficient justification or empirical evidence demonstrating the advantage of diffusion-based generation over traditional GAN-based approaches. \n\n\n*Minor Weaknessess:*\n\n1. The authors defined $C_{t}$ and  $C_{s}$ sometimes as Classifiers and target model line 227 and 228. Can be nice to clarify this aspect. Also the equation 9 need to be clarify as the authors use $C_{t}(T_{s})$ in the two part but for the first part it’s more the prediction of the model and second part for MMD it’s the feature of $T_{s}$ it’s inconsistent.\n\n2. The authors can support the claim line 33 and 34 about \"time consuming and expert interpretation\"\n\n\n\n[1] Domain Impression: A Source Data Free Domain Adaptation Method, $\\textit{Vinod K Kurmi, Venkatesh K Subramanian, Vinay P Namboodiri}$\n\nQUESTIONS:\n1. As previously mentioned, the adaptation component of the second phase of the method is similar to standard practices commonly used in unsupervised domain adaptation [1]. Can the authors clarify the novelty of this part, as it seems limited. The benefits of the proposed approach seem to result essentially from the image generation stage rather than the target model training step.\n\n2. Also, since the advantage of the proposed method seems to rely on the generated images, can the authors explain on why diffusion-based images perform better than those generated by GANs for adaptation? What would happen if images from a GAN were used instead during the second stage of adaptation?\n\n3. Can the authors clarify how their model would perform on more challenging cross-organ adaptation tasks, such as adaptation between GlaS and CAMELYON as defined in [2], or on other datasets involving stronger domain shifts ? \n\n\n4. Also, it appears that training the model requires a reference image, which I assume is selected from the target domain. However, the paper does not explain how the reference image is selected. It would be interesting to understand if selecting differents images impacts the results. Since each image in the target domain may have a different stain matrix, this parameter could influence the method's performance. Can the author explain more precisely this aspect.\n\n\n[2] Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology, $\\textit{Alexis Guichemerre, Soufiane Belharbi, Tsiry Mayet, Shakeeb Murtaza, Pourya Shamsolmoali, Luke McCaffrey, Eric Granger}$\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors tackle stain/style shifts in histopathology with Stain-Guided Cycle Diffusion (SGCD. Two pre-trained diffusion models (one per domain) that translate patches back-and-forth under (i) bidirectional generative constraints and (ii) a stain-guided consistency (SGC) loss. Unlike prior GAN or color-deconvolution approaches, SGCD needs no paired data or hand-picked reference slide and explicitly pushes the reverse process to preserve pathology-relevant structure. On four public datasets, the method consistently outperforms current methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Clever reuse of pre-trained diffusion models plus a stain-aware guidance term. The cyclic bi-directional training is a nice twist that avoids the usual need for paired patches and still keeps semantics intact (although not really new, as this is a known trick first introduced by CycleGAN).\n2. Uses four well-accepted datasets, multiple cross-hospital splits, and compares to 10+ baselines, including the latest diffusion UDA (StainDiff) and medical DG methods\n3. Pathology practitioners struggle with stain heterogeneity; a drop-in diffusion-based adaptor that outperforms current methods is valuable. The approach may generalize to other low-data medical domains.\n4. The paper is mostly easy to follow; pseudo-code and training details (optimizer, timesteps, GPUs) are given, and the code is promised.\n\nWeaknesses:\n1. The method adds overhead. Performing a cycle while training is always a big cost in training run-time and resources.\n2. Minor: No ablation on the (seemingly important) hyper-param k. It would be interesting to see how sensitive the method is to it.\n3. It would be nice to see visually the failure cases (which I'm sure exist) in order to understand better the method.\n\nQUESTIONS:\n1. Why Connect Later performs marginally better in table 2 but in table 3 it doesn't?\n2. Would it generalize to other areas in the medical domain?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims to address the problem of UDA problem for histopathology image classification, where models trained on one domain  stained slides often underperform on another’s due to stain and preparation variations. The authors propose  SGCD, a dual-diffusion generative framework that translates images from source to target domain while preserving crucial morphological features. The SGCD architecture imposes bidirectional generative constraints, which is a cyclic consistency between source to target and target to source translations similar to the cycleGAN, to maintain semantic fidelity during translation. Additionally, a stain-guided consistency loss is introduced to guide the diffusion models and enhance their denoising and translation quality without requiring any paired training images. In addition, the author also use a classification loss to ensure that the source classifier should predict the same label for the reconstructed image. Experiments on four public histopathology datasets demonstrate that SGCD substantially improves target-domain classification performance compared to prior stain normalization and UDA methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The author propose dual-diffusion architecture with bidirectional generative constraints,  which first pushes an image to the opposite domain, then brings it back through another noisy–denoise cycle, similar to the cycleGAN,  the idea seems interesting.\n\n2. The proposed stain-guided loss  steers each denoising step toward a Beer–Lambert–based stain reference while simultaneously enforcing task-level classification consistency, enabling high-quality translation without paired data.\n3. The author use source-trained classifiers to synthesized images, the classification loss make synthesized images remain discriminative for downstream diagnosis.\n4. SGCD delivers state-of-the-art performance on four pathology benchmarks, demonstrating superior stability, reproducibility, and cross-hospital robustness.\n\nWeaknesses:\n\n1. The approach involves training/fine-tuning two diffusion models (for source and target domains) and then generating many synthetic images for classifier training. Diffusion models are resource-intensive, and although the authors fine-tune rather than train from scratch, the method still likely requires substantially more computation than simpler UDA techniques (like feature adaptation or one-pass style transfer). The paper does not report training times or complexity in detail. As a reviewer, I worry about the practicality: e.g., how many diffusion steps k are needed for good results,  some discussion or results on efficiency would strengthen the work.\n\n2. All experiments focus on binary classification tasks (tumor vs. normal or mitosis detection), and mostly on H\\&E stain variations. It remains to be seen how SGCD performs for more complex scenarios, such as multi-class classification or adaptation between entirely different staining protocols (e.g. H\\&E vs. immunohistochemistry).\n\nQUESTIONS:\n1. While you compare to GAN-based and other UDA methods, it would strengthen the work to see an ablation or comparison showing the benefit of dual diffusion and SGC loss  a one-diffusion approach. Are the gains primarily coming from the cycle consistency or from diffusion in general?\n2. The method is described as “stain-guided”, suggesting it’s tailored to color/stain variations. How would SGCD handle domain shifts that are not purely stain differences? For instance, if the target domain has different tissue structures or artifacts (blur, scanner noise) that the source doesn’t, can the dual diffusion still adapt effectively? Please kindly refer to the weakness part and the question part for the author.\n\nLIMITATIONS:\nPlease kindly refer to the weakness part and the question part for the author.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}