An interpretable machine learning system for colorectal cancer diagnosis from pathology slides

arXiv:2301.02608v2 Announce Type: replace-cross
Abstract: Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active …

abstract artificial artificial intelligence arxiv cancer cancer diagnosis cs.cv deep learning diagnosis eess.iv images intelligence labels machine machine learning pathology practice sampling scalable slides strategy transformation type