mlsanityData to 7 Oct 2026

WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma

arXiv 2204.06455 ↗First posted 13 Apr 2022eess.IV cs.CVcited by 32

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Abstract (arXiv)

Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential value of the histopathology images can promote precision medicine in oncology. Tissue segmentation is the basic upstream task of histopathology image analysis. Existing deep learning models have achieved superior segmentation performance but require sufficient pixel-level annotations, which is time-consuming and expensive. To enrich the label resources of LUAD and to alleviate the annotation efforts, we organize this challenge WSSS4LUAD to call for the outstanding weakly-supervised semantic segmentation (WSSS) techniques for histopathology images of LUAD. Participants have to design the algorithm to segment tumor epithelial, tumor-associated stroma and normal tissue with only patch-level labels. This challenge includes 10,091 patch-level annotations (the training set) and over 130 million labeled pixels (the validation and test sets), from 87 WSIs (67 from GDPH, 20 from TCGA). All the labels were generated by a pathologist-in-the-loop pipeline with the help of AI models and checked by the label review board. Among 532 registrations, 28 teams submitted the results in the test phase with over 1,000 submissions. Finally, the first place team achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919). According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS. Cutmix data augmentation has been widely adopted to generate more reliable samples. With the success of this challenge, we believe that WSSS approaches with patch-level annotations can be a complement to the traditional pixel annotations while reducing the annotation efforts. The entire dataset has been released to encourage more researches on computational pathology in LUAD and more novel WSSS techniques.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 37 of its 72 references that are in the map. First posted on arXiv 13 Apr 2022.

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37 of 72 references.

References · 37

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25 more
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Citing papers per year: 2023: 4; 2024: 12; 2025: 10; 2026: 6.

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  1. 2023PathAsst: A Generative Foundation AI Assistant Towards Artificial General Intelligence of Pathologycited by 57
  2. 2023Towards a Visual-Language Foundation Model for Computational Pathologycited by 52
  3. 2024PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathologycited by 43
  4. 2025Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasonercited by 38
  5. 2024CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathologycited by 34
  6. 2024PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaborationcited by 28
  7. 2024DOTA: Distributional Test-Time Adaptation of Vision-Language Modelscited by 22
  8. 2024Knowledge-enhanced Pretraining for Vision-language Pathology Foundation Model on Cancer Diagnosiscited by 21
  9. 2024CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignmentcited by 20
  10. 2023Rotation-Agnostic Image Representation Learning for Digital Pathologycited by 15
  11. 2024Knowledge-enhanced Visual-Language Pretraining for Computational Pathologycited by 14
  12. 2025A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Modelcited by 12
20 more
  1. 2024A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Modelscited by 9
  2. 2025Multi-Modal Foundation Models for Computational Pathology: A Surveycited by 9
  3. 2025PathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Taskscited by 8
  4. 2024A multi-modal vision-language model for generalizable annotation-free pathology localizationcited by 7
  5. 2024Towards a text-based quantitative and explainable histopathology image analysiscited by 7
  6. 2025How Good is my Histopathology Vision-Language Foundation Model? A Holistic Benchmarkcited by 7
  7. 2025Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representationcited by 6
  8. 2025Self-Paced Learning for Images of Antinuclear Antibodiescited by 6
  9. 2024CAMP: Continuous and Adaptive Learning Model in Pathologycited by 4
  10. 2025Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathologycited by 4
  11. 2023CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Imagescited by 2
  12. 2024HisynSeg: Weakly-Supervised Histopathological Image Segmentation via Image-Mixing Synthesis and Consistency Regularizationcited by 2
  13. 2025Superpixel Boundary Correction for Weakly-Supervised Semantic Segmentation on Histopathology Imagescited by 2
  14. 2025UAM: A Unified Attention-Mamba Backbone of Multimodal Framework for Tumor Cell Classificationcited by 1
  15. 2026ReinPath: A Multimodal Reinforcement Learning Approach for Pathologycited by 1
  16. 2026Free Lunch in Medical Image Foundation Model Pre-training via Randomized Synthesis and Disentanglementcited by 0
  17. 2026Margin-Consistent Deep Subtyping of Invasive Lung Adenocarcinoma via Perturbation Fidelity in Whole-Slide Image Analysiscited by 0
  18. 2026From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathologycited by 0
  19. 2026ProBAG: Prototype-Guided Boundary-Aware Graph Diffusion for Weakly Supervised Histopathology Segmentationcited by 0
  20. 2026FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classificationcited by 0