Title: MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Digital pathology

Medical image segmentaton

Semi-supervised Learning
Abstract: In semi-supervised segmentation, capturing meaningful semantic structures from unlabeled data is essential. This is particularly challenging in histopathology image analysis, where objects are densely distributed. To address this issue, we propose a semi-supervised segmentation framework designed to robustly identify and preserve relevant topological features. Our method leverages multiple perturbed predictions obtained through stochastic dropouts and temporal training snapshots, enforcing topological consistency across these varied outputs. This consistency mechanism helps distinguish biologically meaningful structures from transient and noisy artifacts. A key challenge in this process is to accurately match the corresponding topological features across the predictions in the absence of ground truth. To overcome this, we introduce a novel matching strategy that integrates spatial overlap with global structural alignment, minimizing discrepancies among predictions. Extensive experiments demonstrate that our approach effectively reduces topological errors, resulting in more robust and accurate segmentations essential for reliable downstream analysis. Code is available at https://github.com/Melon-Xu/MATCH.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a method with three modules for the segmentation of histopathological images: the first module aims at increasing the consistency across predictions from slightly augmented images (semi-supervised learning); the second module aims at increasing the consistency across multiple stochastic predictions (MC dropout); the third module aims at increasing the topological consistency across different snapshots of the model during training. The method is compared with prior related work, and ablation studies are conducted.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n- Novelty: consistency matching strategies are common in semi-supervised learning, but they are typically focused on pixel-level consistency and not topology consistency, which is novel.\n- Number of experiments: experiments include comparisons with several related modern prior work on three datasets, ablation and sensitivity studies.\n- Importance in microscopy imaging:Since there exist fully-annotated microscopy datasets and annotating new ones is time-consuming, new methods in semi-supervised learning strategies impact scientific areas utilizing microscopy imaging.\n- Clarity: the method is well described.\n\n# Weaknesses\n- Experiments were conducted only once (one random seed).\n- The ablation studies were only conducted on two out of the three components. Specifically, there is an ablation study for the \"intra\" and for the \"temp\" terms, but not for the \"const\" term in the equation above Section 4.\n- The ablation studies show that the method is sensitive to the hyper-parameters (see Table A, B_intra and B_temp value, whose best value was 4; not 3 or 5). It also doesn't seem to be an easy/intuitive way to tune them. Furthermore, it hyper-parameter values probably depend on the dataset.\n- Running time, which is crucial especially in Persistence-Homology-based methods, is unreported.\n\nMinor:\n- Typo in 211: \"EM [48]. UA-MT\" it should be a comma.\n- Typo in L62: \"topolgoical\"\n- L261: This is not an ablation study but a sensitivity study, i.e., the goal is to measure how sensitive the method is to a hyper-parameter choice.\n\nQUESTIONS:\n- What's the exact running time of this method? And what about the running time of a baseline that does not include the persistence-homology method?\n- There are two algorithms to compute the Matches (Section 3.1 and 3.2). Which one is actually used for the final method?\n- How/which statistical significance tests were used?\n\nLIMITATIONS:\nA limitation is mentioned in the supplementary material. I think that there are more obvious limitations with this method. First, relying on persistence homology typically translates to extremely large training time (see one of my questions). Second, since this method relies on different \"modules\" (see the three extra terms in the total loss function, above Section 4), the underperformance on one of them due to dataset particularities could lead to a general underperformance. Additionally, the sensitivity analysis on the hyper-parameters show that an optimal hyper-parameter value depends on the dataset, and there is no easy/intuitive way to tune it.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes MATCH, a semi-supervised segmentation framework specifically designed for histopathology images.  It effectively mitigates topological errors by imposing adaptive topological consistency constraints. A central and novel aspect of this approach is the ability to discern reliable topological structures from multiple perturbed predictions, eliminating the reliance on fixed persistence thresholds, a requirement in prior methods such as TopoSemiSeg. The framework innovatively introduces a dual-level topological consistency mechanism. This includes intra-topological consistency across Monte Carlo dropout predictions and temporal-topological consistency across training snapshots. Moreover, the method utilizes novel matching algorithms, namely MATCH-Pair and MATCH-Global. These algorithms ingeniously integrate spatial overlap, topological persistence, and spatial proximity to accurately align topological structures across different predictions. Comprehensive experiments conducted on three histopathology datasets, namely CRAG, GlaS, and MoNuSeg, vividly demonstrate the superiority of MATCH. The results show notable improvements in both pixel-wise and topology-wise metrics when compared to existing state-of-the-art semi-supervised methods.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n\n1. The paper effectively overcomes a crucial shortcoming of existing topology-aware semi-supervised approaches by eliminating the need for hand-picked persistence thresholds. The adaptive identification of meaningful topological structures is well-motivated and solid from a technical perspective.\n\n2. The dual-level consistency framework is well-designed, combining intra-prediction consistency (MC dropout) with temporal consistency across training epochs. The MATCH-Pair and MATCH-Global algorithms effectively integrate multiple criteria (spatial overlap, persistence, proximity) for robust structure matching.\n\n3. The evaluation is comprehensive, covering three datasets with both pixel-wise and topology-specific metrics. The consistent improvements across different label ratios (10%, 20%) and datasets demonstrate the method's effectiveness.\n\n### Weaknesses\n\n1. Although the method purports to eschew hand-selected thresholds, it incorporates several hyperparameters ( $\\tau_{primary}$) for which the sensitivity has not been comprehensively analyzed. The ablation studies are restricted and do not encompass all crucial parameters.\n2. The paper fails to sufficiently discuss the computational cost of the matching algorithms. In particular, MATCH-Global, which necessitates solving multiple Hungarian assignment problems, might impose a substantial constraint on practical implementation.\n3. The elaboration and pre-explanation of the theory are inadequate. Specifically, the significance of \"Birth\" and \"Death\" depicted in Figure 1 has not been expounded upon. It is further recommended to specify what exactly \"dual-level\" refers to in Figure 3.\n4. The paper has multiple grammatical mistakes and ambiguous expressions. For instance, \"topolgoical\" is misspelled in line 62, and there are issues with inconsistent notation. Also, in certain sections, the mathematical notation could be made more accurate.\n\nQUESTIONS:\n1. Under what conditions does the matching algorithm fail? Can you provide examples where MATCH-Global produces incorrect correspondences and discuss potential mitigation strategies?\n2. It is stated that a novel integration of topological reasoning into SSL is one of the key contributions (Line 66). Nevertheless, the approach presented in this paper appears to be an enhancement of the TopoSemiSeg [57] approach. More evidence should be provided to demonstrate the innovativeness of the integration of topological reasoning and SSL in this paper.\n3. Given that there are some matching algorithms in this paper, could you please provide a detailed computational complexity analysis along with runtime comparisons against well-known or relevant baseline methods?\n4. Could you elaborate on whether and how the dropout rate of Monte Carlo (MC) affects the algorithm proposed in this paper?\n\nLIMITATIONS:\nThe limitations section needs expansion in the main paper to include computational and methodological constraints.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors introduce MATCH, a self-supervised framework for image segmentation in the medical field based on a teacher-student network architecture, where the student learns from both supervised loss on labeled data (standard Dice + cross-entropy) and on unlabeled data (through consistency at the topological level). In particular, it uses dual-level topological consistency via stochastic predictions from Monte Carlo dropout and temporal training snapshots. This process allows the framework to learn useful representations.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe topological consistency is enforced by MATCH-pair, a Hungarian overlap-matching algorithm that integrates spatial overlap, topological persistence, and spatial proximity, which is rather interesting and well-motivated.\n\nThe contribution grounded in geometric intuition is supported by good uncertainty maps (Figure 5) and improved segmentation results (Figure 6).\n\nThe evaluation on three datasets is also quite thorough, and the proposed model seems to consistently outperform others.\n\nWeaknesses:\nThe results are only marginally better than the best current method in terms of segmentation quality. The Dice score is often just 0.001–0.006 higher than TopoSemiSeg, which is the main weakness in my opinion.\n\nThe computational cost of the topological loss in terms of model training and inference is not clearly described. How does the cost-value ratio look, particularly considering the marginal improvement in performance?\n\nThe authors do not compare their method with self-supervised approaches like SAM and its variants such as CellVit. Fine-tuning an SSL method on limited labeled data is also considered semi-supervision.\n\nThe novelty of the proposed method is not very high, as it is a combination of modules that are based on existing methods.\n\nQUESTIONS:\nWhat is the computational cost of the topological loss in terms of speed and memory consumption, particularly compared to other competitive methods like TopoSemiSeg, which uses Betti matching?\n\nThe main results in Table 1 do not show where your method saturates (there is still quite a difference between the 20% labeled and fully supervised results). How much can the unsupervised loss replace the contribution of supervised loss in training?\n\nHow is the generated uncertainty map correlated to real-world segmentation uncertainty, e.g., segmentation annotations by two experts?\n\nLIMITATIONS:\nOne limitation I can think of is that the choice of hyperparameters in the model is somewhat equivalent to manually setting up thresholds for TopoSemiSeg, which the authors criticise and present as one primary motivation for their method. How stable are those hypoparameters?", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors aim at improving histopathology image segmentation when unlabeled data are predominant during training. For this reason, they propose a Semi-Supervised Learning (SSL) image segmentation framework and enforce image segmentation prediction robustness against perturbations adopting topological reasoning. In particular, the authors model perturbations with Monte Carlo dropout mechanism and by considering the predictions in different training epochs. As for the topological consistency, they formulate the structure correspondence task as a contrastive learning problem to distinguish features considered stable when matching per spatial overlap, topological persistence and spatial proximity criteria across themselves (via MATCH-Pair) and across multiple predictions (via MATCH-Global). The method is validated in the task of binary gland and cell segmentation across 3 different datasets and with different percentages of label data, ablation study correlates the results analysis.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n- The proposed method appears technically sound and even though it does not introduce an entirely new method (SSL, contrastive learning paradigm, persistent diagrams, Monte Carlo dropout perturbations, consistency between different training temporal views, Hungarian matching logic are already known technique and widely used in the state of the art), their usage context and combination appears in this work original and more efficient than other state-of-the-art methods (i.e. exploiting the persistent structures between perturbations allow avoiding hand-picking threshold to determine reliable structures as happening in TopoSemiSeg).  Notably, the MATCH-Pair and MATCH-Global can be considered a smart glue to define valid matches between 2 persistent diagrams and between multiple facets, respectively. \n\nWeaknesses: \n1. The authors state that they use a pretrained model to initialize the SSL training. However, it is unclear whether this pretraining was performed using only the limited labeled data available to the student model, or if additional labeled data were used. If the teacher model is pretrained on a larger labeled dataset than what is available during the SSL phase, this would violate the core assumption of semi-supervised learning, namely, that only a small fraction of labeled data is accessible. In such a case, the observed performance gains may stem from the additional supervision during pretraining rather than from the proposed SSL strategy itself.\n2. The authors state that capturing meaningful semantic structures from unlabeled data is essential, particularly in scenarios where objects are densely distributed (l. 3). They also acknowledge that such dense distributions often lead to topological errors (l. 21). This is indeed a critical aspect in histopathology image segmentation. However, despite choosing the MoNuSeg dataset, which contains patches with densely packed nuclei, the authors do not analyze how the model's performance varies with respect to object crowding.\n3. The authors evaluate their method on a combination of binary (CRAG) and inherently multiclass (GlaS and MoNuSeg) datasets, but convert all tasks into binary segmentation. While the decision to reduce the problem to a binary setting is understandable given the technical challenges of extending semi-supervised learning to multiclass segmentation, it nonetheless limits the scope of the evaluation and the relevance of the results to real-world histopathology applications. More critically, the method does not account for instance-level structure, despite the authors’ emphasis on the challenges posed by densely packed cellular objects. Unlike the multiclass case, instance-level information is essential for properly resolving overlapping nuclei, and omitting this consideration weakens the effectiveness of the proposed topological reasoning. Topological consistency alone may struggle to disambiguate closely spaced structures without explicit instance-level constraints.\n4. The variables $d_i$ and $b_i​$, first introduced at line 147, are never explicitly defined in the manuscript. While their meaning may be inferred by readers familiar with SSL literature (the birth and depth points in the persistence diagrams), this lack of definition may hinder understanding for a broader audience. For clarity and completeness, all variables, especially those central to the method, should be explicitly defined when first introduced\n5. The authors do not clearly specify the source of the likelihood maps $lh_{1}$ $lh_{2}$ (l. 147) specifically, whether these correspond to the output of a sigmoid activation applied to the final layer of the UNet. Clarifying this detail would improve both the clarity and reproducibility of the method, as the exact definition of these maps is critical for understanding how predictions are calibrated and how consistency is enforced during training.\n6. It is unclear whether the index notation $i,j$,  used at line 153 is correct in the given context. Additionally, the indices $i,j,k,$ are never explicitly defined in the manuscript, which may cause confusion, especially for readers trying to follow the mathematical formulation in detail. Providing a clear definition of the indexing convention would improve the rigor and readability of the method description.\n\nMinor Weaknesses: \n1. line 62 typo topolgoical\n2. since Figure 3 is referenced before $L_{intra}$ and $L_{temp}$ it is better to repeat their name definition in the caption\n3. line 177 typo: missing {\n4. line 177 fragmented sentence: G identified…\n5. line 196 format typo inconsistency with Eq. at line 195: $P^{(t)}bt, i$ and $P^{(t)}dt, i$\n\nQUESTIONS:\n1. Could the authors clarify whether the teacher model used in the SSL framework was pretrained using only the same limited set of labeled data available to the student? If additional labeled data were used during pretraining, how do the authors justify this in the context of a semi-supervised learning setup, where strict limitations on label availability are assumed?\n2. Given the authors’ emphasis on the challenges posed by densely packed structures, have they evaluated how the model’s performance varies with object density (e.g., comparing sparse vs. crowded regions in MoNuSeg)? Would the authors consider including a stratified analysis or crowding-aware ablation to validate their method's robustness under varying tissue densities?\n3. While simplifying all tasks to binary segmentation may ease implementation, have the authors considered extending or evaluating their method in the multiclass setting, particularly for GlaS and MoNuSeg? More critically, given the importance of separating overlapping nuclei in histopathology, why was instance-level structure not considered, either during training or evaluation? Could the topological matching mechanism benefit from incorporating explicit instance constraints to better resolve ambiguous or overlapping objects?\n4. Could the authors define the variables $d_i$ and $b_i​$ more clearly when first introduced (l. 147), especially for readers unfamiliar with persistent homology?\n5. What is the precise definition and origin of the likelihood maps $lh1$​ and $lh2$? Do these correspond to the sigmoid-activated outputs of the final UNet layer, or are they derived differently?\n6. At line 153, is the use of the index notation $i,j $ consistent with the dimensions and semantics of the features being described? Furthermore, could the authors clarify the role of the indices $i,j,k$, as they are used without explicit definition and may confuse readers interpreting the formulation?\n\nLIMITATIONS:\nThe authors have adequately addressed the potential negative societal impact of their work", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a semi-supervised segmentation method for histopathology images to resolve the problem of topological errors. It enforces topological consistency across different outputs, including outputs from stochastic dropouts and temporal training snapshots. The experiment shows that it reduces topological errors compared with other methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. This paper proposes a innovative matching algorithm between a pair of perturbed predictions, which solves topological errors.\n2. This paper provides a new view of the reliability of the segmentation predictions.\n\nWeaknesses:\n1. The concept of temporal topological consistency has been explored in semi-supervised learning, so I think as a standalone module it lacks novelty.\n2. The paper only extracts 0-D topological features for matching, without considering structures with holes or annular structures, which commonly exist in histopathology images.\n3. The method’s performance is only validated on three datasets restricted to gland and nucleus segmentation tasks, with no validation on other pathological tasks.\n4. The method relies solely on MC dropout for generating perturbed predictions without comparing it with other perturbation methods, which may limit the model's performance.\n5. The method generates multiple perturbations for each input image through Monte Carlo dropout, increase computational time and memory consumption, especially when dealing with whole-slide images (WSI).\n\nQUESTIONS:\n1. Beyond the three datasets (CRAG, GlaS, MoNuSeg), how does the model generalize to other pathology tasks?\n2. The paper only extracts 0-D topological features. Do you plan to extend the framework to 1-D or higher-order topological features?\n3. Have you evaluated the risk of error propagation in pseudo-labels?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}