  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.
