COREMIL: Contextual Position Encoding-based Retrievable Multiple Instance Learning for Slide-level Classification

Abstract

Multiple Instance Learning (MIL) consists of two stages: feature encoding of instances and feature fusion of instances. This paper identifies two issues in the feature fusion stage of MIL when applied to pathological image classification, which creates performance bottlenecks. First, previous MIL methods lack positional encoding in the feature fusion stage. However, the number of positive instances is critical for pathological diagnosis. This makes it difficult for earlier MIL models to perceive positive instances, limiting their ability to capture the semantic correlation between the number of positive instances and the disease. Second, previous MIL methods base both inference and training on a single Whole Slide Image (WSI), failing to utilize cross-slide information effectively. To address these issues, this paper proposes a novel attention mechanism called CORE Attention (Contextual Position Encoding-based Retrievable Attention) during the instance fusion stage of MIL, and develops the COREMIL model based on it. CORE Attention consists of two modules: contextual position encoding and cross-slide retrieval-based attention fine-tuning. The contextual position encoding captures better contextual information, especially for counting tasks. Cross-slide retrieval-based attention fine-tuning allows the model to leverage previously learned historical information to guide the attention in the current feature fusion process. This paper validated COREMIL’s classification performance on several public datasets and a private dataset of pathological images. Extensive experiments demonstrate that COREMIL outperforms other current MIL models in terms of F1 Score and AUC on most datasets, with improvements of up to 14.414%. This CORE Attention-based approach offers an efficient solution for slide-level classification problems. Our code will be accessed shortly.