TKA-MIL: Top-K Attention Multiple Instance Learning for Whole Slide Image Classification and Instance Probability Derivation

Abstract

Multiple instance learning (MIL) has been increasingly applied to the classification of histopathology whole slide images (WSI). While many classic attention-based MIL algorithms have achieved good classification performance, they do not account for the relationships between instances. Using transformers in MIL can effectively capture these relationships but requires substantial computational resources. To address the trade-off between performance and computational cost, we propose a new simplified attention-based MIL model—TKA-MIL, which only capture the relationships between all instances and critical instances. Additionally, we derive the probability of an instance being positive based on the attention weights of TKA-MIL. Experiments on the Camelyon16 and TCGA Lung Cancer datasets demonstrate that our approach achieves an optimal balance between classification performance and model complexity, and the derived instance probabilities are more accurate compared to directly using normalized attention weights.