Self-interactive learning: Fusion and evolution of multi-scale histomorphology features for molecular traits prediction in computational pathology

作者：Yang Hu, Korsuk Sirinukunwattana, Bin Li, Kezia Gaitskell, Enric Domingo, Willem Bonnaffé, Marta Wojciechowska, Ruby Wood, Nasullah Khalid Alham, Stefano Malacrino, Dan J. Woodcock, Clare Verrill, Ahmed A. Ahmed, Jens Rittscher · 发表于：Medical Image Analysis · 年份：2025 · DOI：10.1016/j.media.2024.103437 · 被引用次数：5 · 研究领域：AI in cancer detection、Gene expression and cancer classification、Digital Imaging for Blood Diseases

Predicting disease-related molecular traits from histomorphology brings great opportunities for precision medicine. Despite the rich information present in histopathological images, extracting fine-grained molecular features from standard whole slide images (WSI) is non-trivial. The task is further complicated by the lack of annotations for subtyping and contextual histomorphological features that might span multiple scales. This work proposes a novel multiple-instance learning (MIL) framework capable of WSI-based cancer morpho-molecular subtyping by fusion of different-scale features. Our method, debuting as Inter-MIL, follows a weakly-supervised scheme. It enables the training of the patch-level encoder for WSI in a task-aware optimisation procedure, a step normally improbable in most existing MIL-based WSI analysis frameworks. We demonstrate that optimising the patch-level encoder is crucial to achieving high-quality fine-grained and tissue-level subtyping results and offers a significant improvement over task-agnostic encoders. Our approach deploys a pseudo-label propagation strategy to update the patch encoder iteratively, allowing discriminative subtype features to be learned. This mechanism also empowers extracting fine-grained attention within image tiles (the small patches), a task largely ignored in most existing weakly supervised-based frameworks. With Inter-MIL, we carried out four challenging cancer molecular subtyping tasks in the context of ovarian, colorectal,...