Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis

Xi Wang, Hao Chen, Caixia Gan, Huangjing Lin, Qi Dou, Efstratios Tsougenis, Qitao Huang, Muyan Cai et al.

IEEE Transactions on Cybernetics 2019 article cited by 408

Source More in Medicine & Health

importance 6/10 ai assisted AI in cancer detection digital pathology stage: research

Problem

Whole-slide lung cancer image classification is blocked by scarce pixel-level annotations, heterogeneous tumor patterns, and gigapixel image sizes.

Why it matters

Histopathology is the gold standard for cancer diagnosis, so faster accurate analysis affects patient treatment.

What the AI did

Weakly supervised deep learning methods are designed to exploit whole-slide-level labels for WSI classification without pixelwise annotation.

Results

not stated in source

Impact

Evidence supports a method addressing annotation scarcity; no measured results or clinical impact stated in abstract.

Record

Venue	IEEE Transactions on Cybernetics
Published	2019
Work type	article
Authors	Xi Wang, Hao Chen, Caixia Gan, Huangjing Lin, Qi Dou, Efstratios Tsougenis, Qitao Huang, Muyan Cai et al.
Citations	408 (OpenAlex, at import time)
References	67
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1109/tcyb.2019.2935141
Open access	closed
Keywords	whole slide imaging, weakly supervised learning, lung cancer, histopathology, deep learning

Importance rating

6 / 10

significant — thousands to millions affected

Histopathology is the gold standard for cancer diagnosis and efficient diagnosis affects patient treatment; weakly supervised WSI classification addresses annotation scarcity, but no results or scale are stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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