Renal Cell Carcinoma subtyping: learning from multi-resolution localization

TL;DR

This paper proposes a self-supervised learning approach leveraging multi-resolution analysis to classify renal cell carcinoma subtypes from histological images, aiming to reduce annotation needs while maintaining high accuracy.

Contribution

It introduces a novel self-supervised training strategy that exploits multi-resolution features for renal cancer subtyping, reducing reliance on annotated datasets.

Findings

Effective classification on whole slide images.

Comparable accuracy to state-of-the-art supervised methods.

Reduced need for annotated data.

Abstract

Renal Cell Carcinoma is typically asymptomatic at the early stages for many patients. This leads to a late diagnosis of the tumor, where the curability likelihood is lower, and makes the mortality rate of Renal Cell Carcinoma high, with respect to its incidence rate. To increase the survival chance, a fast and correct categorization of the tumor subtype is paramount. Nowadays, computerized methods, based on artificial intelligence, represent an interesting opportunity to improve the productivity and the objectivity of the microscopy-based Renal Cell Carcinoma diagnosis. Nonetheless, much of their exploitation is hampered by the paucity of annotated dataset, essential for a proficient training of supervised machine learning technologies. This study sets out to investigate a novel self supervised training strategy for machine learning diagnostic tools, based on the multi-resolution nature of the histological samples. We aim at reducing the need of annotated dataset, without significantly reducing the accuracy of the tool. We demonstrate the classification capability of our tool on a whole slide imaging dataset for Renal Cancer subtyping, and we compare our solution with several state-of-the-art classification counterparts.