mlsanityData to 14 Oct 2026

Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labels

arXiv 2110.08048 ↗First posted 14 Oct 2021eess.IV cs.CV q-bio.QMcited by 43

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Abstract (arXiv)

Tissue-level semantic segmentation is a vital step in computational pathology. Fully-supervised models have already achieved outstanding performance with dense pixel-level annotations. However, drawing such labels on the giga-pixel whole slide images is extremely expensive and time-consuming. In this paper, we use only patch-level classification labels to achieve tissue semantic segmentation on histopathology images, finally reducing the annotation efforts. We proposed a two-step model including a classification and a segmentation phases. In the classification phase, we proposed a CAM-based model to generate pseudo masks by patch-level labels. In the segmentation phase, we achieved tissue semantic segmentation by our proposed Multi-Layer Pseudo-Supervision. Several technical novelties have been proposed to reduce the information gap between pixel-level and patch-level annotations. As a part of this paper, we introduced a new weakly-supervised semantic segmentation (WSSS) dataset for lung adenocarcinoma (LUAD-HistoSeg). We conducted several experiments to evaluate our proposed model on two datasets. Our proposed model outperforms two state-of-the-art WSSS approaches. Note that we can achieve comparable quantitative and qualitative results with the fully-supervised model, with only around a 2% gap for MIoU and FwIoU. By comparing with manual labeling, our model can greatly save the annotation time from hours to minutes. The source code is available at: urlhttps://github.com/ChuHan89/WSSS-Tissue.

Key facts. Cited by 43 papers in the map (data through 14 Oct 2026). Builds on 39 of its 75 references that are in the map. First posted on arXiv 14 Oct 2021.

Builds on

39 of 75 references.

References · 39

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  2. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  3. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
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  5. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,057
  6. 2017Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultscited by 472
  7. 2018HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Imagescited by 388
  8. 2019S4L: Self-Supervised Semi-Supervised Learningcited by 335
  9. 2016ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentationcited by 331
  10. 2018Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentationcited by 321
  11. 2020Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentationcited by 300
  12. 2015What's the Point: Semantic Segmentation with Point Supervisioncited by 298
27 more
  1. 2015BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentationcited by 238
  2. 2016Wider or Deeper: Revisiting the ResNet Model for Visual Recognitioncited by 184
  3. 2019Deep neural network models for computational histopathology: A surveycited by 165
  4. 2015Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classificationcited by 159
  5. 2019A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysiscited by 151
  6. 2017Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentationcited by 146
  7. 2020Weakly-Supervised Semantic Segmentation via Sub-category Explorationcited by 145
  8. 2016DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentationcited by 84
  9. 2020Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptationcited by 81
  10. 2021Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learningcited by 78
  11. 2018Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Networkcited by 74
  12. 2020Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Modelcited by 59
  13. 2020Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Imagescited by 55
  14. 2020Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistencycited by 52
  15. 2017Constrained Deep Weak Supervision for Histopathology Image Segmentationcited by 51
  16. 2020Weakly supervised multiple instance learning histopathological tumor segmentationcited by 46
  17. 2020HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide imagescited by 45
  18. 2020Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentationcited by 42
  19. 2021SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learningcited by 35
  20. 2018Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Featurescited by 28
  21. 2021DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentationcited by 27
  22. 2020Deep Active Learning for Joint Classification & Segmentation with Weak Annotatorcited by 16
  23. 2020Pairwise Relation Learning for Semi-supervised Gland Segmentationcited by 13
  24. 2020Negative Pseudo Labeling using Class Proportion for Semantic Segmentation in Pathologycited by 12
  25. 2020Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Imagescited by 12
  26. 2020Self-similarity Student for Partial Label Histopathology Image Segmentationcited by 11
  27. 2021WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Imagescited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 7; 2024: 10; 2025: 13; 2026: 10.

43 papers.

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  1. 2023CLIP in Medical Imaging: A Surveycited by 40
  2. 2022WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinomacited by 36
  3. 2025Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentationcited by 17
  4. 2024A Survey of Medical Vision-and-Language Applications and Their Techniquescited by 11
  5. 2023Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancercited by 10
  6. 2025UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretationcited by 10
  7. 2025How Good is my Histopathology Vision-Language Foundation Model? A Holistic Benchmarkcited by 8
  8. 2024A multi-modal vision-language model for generalizable annotation-free pathology localizationcited by 7
  9. 2024WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide imagescited by 7
  10. 2024Benchmarking PathCLIP for Pathology Image Analysiscited by 6
  11. 2025SOTA: Self-adaptive Optimal Transport for Zero-Shot Classification with Multiple Foundation Modelscited by 6
  12. 2023FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classificationcited by 3
31 more
  1. 2023UM-CAM: Uncertainty-weighted Multi-resolution Class Activation Maps for Weakly-supervised Fetal Brain Segmentationcited by 3
  2. 2023Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Imagescited by 3
  3. 2024From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labelscited by 3
  4. 2025PathoPainter: Augmenting Histopathology Segmentation via Tumor-aware Inpaintingcited by 3
  5. 2025PathSegDiff: Pathology Segmentation using Diffusion model representationscited by 3
  6. 2023CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Imagescited by 2
  7. 2024HisynSeg: Weakly-Supervised Histopathological Image Segmentation via Image-Mixing Synthesis and Consistency Regularizationcited by 2
  8. 2025Superpixel Boundary Correction for Weakly-Supervised Semantic Segmentation on Histopathology Imagescited by 2
  9. 2025Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentationcited by 2
  10. 2025LPD: Learnable Prototypes with Diversity Regularization for Weakly Supervised Histopathology Segmentationcited by 2
  11. 2026VISTA-PATH: An interactive foundation model for pathology image segmentation and quantitative analysis in computational pathologycited by 2
  12. 2023BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applicationscited by 1
  13. 2024Enhancing Weakly-Supervised Histopathology Image Segmentation with Knowledge Distillation on MIL-Based Pseudo-Labelscited by 1
  14. 2024EP-SAM: Weakly Supervised Histopathology Segmentation via Enhanced Prompt with Segment Anythingcited by 1
  15. 2025Towards Computation- and Communication-efficient Computational Pathologycited by 1
  16. 2026Weakly Supervised Teacher-Student Framework with Progressive Pseudo-mask Refinement for Gland Segmentationcited by 1
  17. 2022Preparing data for pathological artificial intelligence with clinical-grade performancecited by 0
  18. 2022Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classificationcited by 0
  19. 2024HistoSegCap: Capsules for Weakly-Supervised Semantic Segmentation of Histological Tissue Type in Whole Slide Imagescited by 0
  20. 2024Competing for pixels: a self-play algorithm for weakly-supervised segmentationcited by 0
  21. 2025Open-source framework for detecting bias and overfitting for large pathology imagescited by 0
  22. 2025Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Imagescited by 0
  23. 2025ConStruct: Structural Distillation of Foundation Models for Prototype-Based Weakly Supervised Histopathology Segmentationcited by 0
  24. 2026Margin-Consistent Deep Subtyping of Invasive Lung Adenocarcinoma via Perturbation Fidelity in Whole-Slide Image Analysiscited by 0
  25. 2026Semantic Segmentation for Histopathology using Learned Regularization based on Global Proportionscited by 0
  26. 2026One Stone, Three Birds: Self-adaptive Optimal Transport for Multi-VLM Selection, Adaptation, and Ensemblingcited by 0
  27. 2026SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathologycited by 0
  28. 2026Single-Stage Hierarchical Rectification for Weakly Supervised Histopathology Segmentationcited by 0
  29. 2026C2RM-Seg: Causal Counterfactual Reasoning with Structural-Semantic Priors for Weakly Supervised Histopathological Tissue Segmentationcited by 0
  30. 2026Toward Efficient Weakly Supervised Semantic Segmentation Using Only Low-Magnification Histopathological Imagescited by 0
  31. 2026Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendationscited by 0