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

• URL: http://arxiv.org/abs/2301.02608v2
• Date: Tue, 30 Apr 2024 18:10:32 GMT
• Title: An interpretable machine learning system for colorectal cancer diagnosis from pathology slides
• Authors: Pedro C. Neto, Diana Montezuma, Sara P. Oliveira, Domingos Oliveira, João Fraga, Ana Monteiro, João Monteiro, Liliana Ribeiro, Sofia Gonçalves, Stefan Reinhard, Inti Zlobec, Isabel M. Pinto, Jaime S. Cardoso,
• Abstract summary: This study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists.
• Score: 2.7968867060319735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.

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