Pathology AlphaGo

[PMID:27527408] [Nature Communications]

Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

“we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors”. Work from Daniel Rubin & Michael Snyder.

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