IMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images.

ABSTRACT: Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WS

SUBMITTER: Neto PC 

PROVIDER: S-EPMC9139905 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature