Sm: enhanced localization in Multiple Instance Learning for medical imaging classification

TL;DR

This paper introduces a novel local dependency modeling mechanism for Multiple Instance Learning in medical imaging, significantly improving localization accuracy while maintaining strong classification performance.

Contribution

It proposes a simple, flexible local dependency module that enhances MIL models for better instance-level localization in medical imaging.

Findings

State-of-the-art localization performance achieved

Competitive or superior classification results

Effective modeling of local dependencies

Abstract

Multiple Instance Learning (MIL) is widely used in medical imaging classification to reduce the labeling effort. While only bag labels are available for training, one typically seeks predictions at both bag and instance levels (classification and localization tasks, respectively). Early MIL methods treated the instances in a bag independently. Recent methods account for global and local dependencies among instances. Although they have yielded excellent results in classification, their performance in terms of localization is comparatively limited. We argue that these models have been designed to target the classification task, while implications at the instance level have not been deeply investigated. Motivated by a simple observation -- that neighboring instances are likely to have the same label -- we propose a novel, principled, and flexible mechanism to model local dependencies. It can be used alone or combined with any mechanism to model global dependencies (e.g., transformers). A thorough empirical validation shows that our module leads to state-of-the-art performance in localization while being competitive or superior in classification. Our code is at https://github.com/Franblueee/SmMIL.