Uncertainty-driven hybrid-view adaptive learning for fully automated uterine leiomyosarcoma diagnosis

Abstract

Uterine leiomyosarcoma (ULMS) is a rare malignant tumor of the smooth muscle of the uterine wall that is aggressive and has a poor prognosis. Accurately and automatically classifying histopathological whole-slide images (WSIs) is critical for clinically diagnosing ULMS. However, few works have investigated automated ULMS diagnosis methods due to its high degrees of concealment and phenotype diversity. In this study, we present a novel uncertainty-driven hybrid-view adaptive learning (UHAL) framework to efficiently capture the distinct features of ULMS by mining pivotal biomarkers at the cell level and minimizing the redundancy from hybrid views under an uncertainty discrimination mechanism, ultimately ensuring reliable diagnoses of ULMS WSIs. Specifically, hybrid-view adaptive learning incorporates three modules: phenotype-driven patch self-optimization to select salient patch features, unsupervised inter-bags adaptive learning effectively filters out redundant information, and compensatory inner-level adaptive learning further refines tumor features. Furthermore, the uncertainty discrimination mechanism achieves enhanced reliability by assigning quantitative confidence coefficients to predictions under the Dirichlet distribution, leveraging uncertainty to update the features for obtaining accurate diagnoses. The experimental results obtained on the ULMS dataset indicate the superior performance of the proposed framework over that of ten state-of-the-art methods. Extensive experimental results obtained on the TCGA-Esca, TCGA-Lung, and Spinal infection datasets further validate the robustness and generalizability of the UHAL framework.