Renal Cell Carcinoma subtyping: Learning from multi-resolution localization

Abstract

International audienceBackground and Objective Renal Cell Carcinoma (RCC) is often diagnosed at advanced stages, limitingtreatment options. Since prognosis depends on tumour subtype, accurate and efficient classification is essential.Artificial intelligence tools can assist diagnosis, yet their dependence on large annotated datasets hindersbroader adoption. This study investigates a Self-Supervised Learning (SSL) framework that exploits the multiresolution structure of Whole histological Slide Images (WSIs) to reduce annotation requirements whilemaintaining reliable diagnostic performance.Methods: We developed a SSL model inspired by the pathologist’s multi-scale reasoning, integrating information across magnification levels. Robustness and generalization were evaluated through an external validationon a public RCC benchmark and one internal validation using cohorts from the same institution but collectedin different periods, with distinct scanners and laboratory workflows.Results and Conclusions The proposed SSL approach demonstrated stable classification performance acrossall validation settings, reducing dependence on manual labels and improving robustness under heterogeneousacquisition conditions. These findings support its potential as a generalizable and annotation-efficient strategyfor RCC subtype classification