A Mixture-of-Experts Decision Support System for Digital Pathology

Abstract

Whole slide image classification is a core task in digital pathology that can assist decision-making procedures for pathologists. Several models, mainly built based upon multiple-instance learning, have shown to be effective in processing and analyzing WSIs. However, these are designed, trained, and evaluated on a single classification task, and thus the models are limited to a specific task and cannot utilize the data and knowledge from other tasks. This substantially limits the ability and expandability of the model to support clinical decision-making. In this study, we present a mixture-of-experts decision support system for digital pathology. The proposed decision support system merges multiple individual models, of which each is tailored to a specific task, and forms a unified model equipped with group intelligence that can handle multiple classification tasks. The proposed system utilizes Transformer architecture to process WSIs and a language decoder to enable flexible classification across multiple tasks. The experimental results on five datasets, including CAMELYON16, TCGA-BRCA, TCGA-NSCLC, TCGA-RCC, and TCGA-ESCA, demonstrate the effectiveness of the proposed approach in supporting clinical decision-making.