Title: Efficient Patch Search in Whole Slide Images via Morphological Momentum Prototype Learning
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Whole Slide Image

Abstract: Digital histopathology images play a crucial role in cancer diagnosis, therapeutic response prediction, and identification of clinically relevant morphological features. However, processing Whole Slide Images (WSI) with gigapixel resolution introduces significant challenges in computer vision, exceeding the memory capacity of standard vision encoders. To address this, recent methods employ a multi-stage pipeline: dissecting the image into small patches, extracting patch-level features, and aggregating these features using global pooling through Multi-Instance Learning (MIL) to form a final slide-level representation. Despite achieving clinical-grade performance, this approach becomes increasingly complex with higher magnification due to the quadratic increase in patch numbers and the generation of numerous irrelevant or redundant patches. This complexity burdens the global pooling network, resulting in long inference times and excessive computational resources, while redundant patches introduce noise during the MIL process, limiting the model's ability to utilize high-magnification features fully. To overcome these challenges, we propose MMPL, an efficient method that redefines WSI diagnosis as a searching process of relevant patch-level representations with a learned set of global prototypes. MMPL trains a fixed set of prototypes to retrieve the most informative patches, computing the diagnostic score using only the retrieved patches. Evaluated on multiple public WSI classification benchmarks, MMPL achieves state-of-the-art performance in accuracy across various pathology tasks, including tumor subtyping, grading, and metastasis detection.