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

Francisco M Castro-Macías, Pablo Morales-Alvarez, Yunan Wu, Rafael Molina, Aggelos Katsaggelos

Keywords: Multiple Instance Learning, Transformers, Graph Neural Networks, Medical Imaging, Weakly Supervised Learning

TL;DR: We draw attention to the localization task in MIL and propose Sm, a principled operator to account for local interactions among instances that yields enhanced performance.

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.

Primary Area: Machine learning for healthcare

Submission Number: 3482