Weakly supervised learning in thymoma histopathology classification: an interpretable approach.

Abstract

Introduction: Thymoma classification is challenging due to its diverse morphology. Accurate classification is crucial for diagnosis, but current methods often struggle with complex tumor subtypes. This study presents an AI-assisted diagnostic model that combines weakly supervised learning with a divide-and-conquer multi-instance learning (MIL) approach to improve classification accuracy and interpretability.

Methods: We applied the model to 222 thymoma slides, simplifying the five-class classification into binary and ternary steps. The model features an attention-based mechanism that generates heatmaps, enabling visual interpretation of decisions. These heatmaps align with clinically validated morphological differences between thymoma subtypes. Additionally, we embedded domain-specific pathological knowledge into the interpretability framework.

Results: The model achieved a classification AUC of 0.9172. The generated heatmaps accurately reflected the morphological distinctions among thymoma subtypes, as confirmed by pathologists. The model's transparency allows pathologists to visually verify AI decisions, enhancing diagnostic reliability.

Discussion: This model offers a significant advancement in thymoma classification, combining high accuracy with interpretability. By integrating weakly supervised learning, MIL, and attention mechanisms, it provides an interpretable AI framework that is applicable in clinical settings. The model reduces the diagnostic burden on pathologists and has the potential to improve patient outcomes by making AI tools more transparent and clinically relevant.