Pith. sign in

REVIEW 6 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2204.06455 v2 pith:Q5X3S2HR submitted 2022-04-13 eess.IV cs.CV

Chu Han , Xipeng Pan , Lixu Yan , Huan Lin , Bingbing Li , Su Yao , Shanshan Lv , Zhenwei Shi
show 35 more authors
Jinhai Mai Jiatai Lin Bingchao Zhao Zeyan Xu Zhizhen Wang Yumeng Wang Yuan Zhang Huihui Wang Chao Zhu Chunhui Lin Lijian Mao Min Wu Luwen Duan Jingsong Zhu Dong Hu Zijie Fang Yang Chen Yongbing Zhang Yi Li Yiwen Zou Yiduo Yu Xiaomeng Li Haiming Li Yanfen Cui Guoqiang Han Yan Xu Jun Xu Huihua Yang Chunming Li Zhenbing Liu Cheng Lu Xin Chen Changhong Liang Qingling Zhang Zaiyi Liu
This is my paper
classification eess.IVcs.CV
keywords annotationschallengeluadsegmentationwssshistopathologypatch-leveltissue
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation

    eess.IV 2026-05 unverdicted novelty 7.0 of 10

    PulmoFoundation achieves 92.3% average AUC on 32 lung pathology tasks in prospective validation and raises pathologist accuracy from 83.8% to 91.7% in a crossover RCT.

  2. From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathology

    cs.CV 2026-08 conditional novelty 6.0 of 10

    MRPT, a multi-resolution hierarchical transformer pre-trained on 36K whole-slide images, is reported to outperform prior pathology foundation models on 34 classification, captioning, and VQA datasets.

  3. ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts

    cs.CV 2026-07 accept novelty 6.0 of 10

    Multi-stage agglomerative distillation consolidates eight vision, vision-language, and slide-level pathology teachers into one backbone that ranks first on average across 96 downstream tasks.

  4. A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model

    eess.IV 2025-07 conditional novelty 6.0 of 10

    A single pathology vision-language model trained with supervised and reinforcement fine-tuning beats four prior MLLMs across 72 ROI-level and slide-level tasks.

  5. A tissue and cell-level annotated H&E and PD-L1 histopathology image dataset in non-small cell lung cancer

    q-bio.QM 2025-07 accept novelty 6.0 of 10

    The IGNITE data toolkit provides 887 annotated regions from 155 patients for H&E tissue segmentation, nuclei detection, and PD-L1 positive tumor cell detection in NSCLC, including metastatic sites.

  6. Segment Anything in Pathology Images with Natural Language

    cs.CV 2025-06 conditional novelty 6.0 of 10

    PathSegmentor is a text-prompted segmentation foundation model for pathology images, trained on a new 160-category, 275k-sample benchmark, and reports higher Dice scores than specialized and prior foundation baselines.

Pith tools