AI and the World's Hard Problems · Medicine & drug discovery · Nature (computational biology) · 2026-10-17
An interpretable hierarchical deep learning framework for lung and colon cancer detection
Classifying lung and colon cancer from histopathology images is difficult because the classes look similar and tissue morphology varies, and conventional deep learning models offer little transparency. The authors split the five-class task into coarse-to-fine stages: first normal versus cancer, then a specialized classifier for normal-tissue categories or cancer subtypes, with the best architecture chosen for each stage. The framework reached 98.50% accuracy on LC25000 and 82.01% on the independent LungHist700 dataset, and Grad-CAM, Grad-CAM++, LIME and Occlusion Sensitivity were used to highlight the image regions behind its predictions.