A Spatially-Aware Multiple Instance Learning Framework for Digital Pathology
Journal:
arXiv
Published Date:
Apr 24, 2025
Abstract
Multiple instance learning (MIL) is a promising approach for weakly
supervised classification in pathology using whole slide images (WSIs).
However, conventional MIL methods such as Attention-Based Deep Multiple
Instance Learning (ABMIL) typically disregard spatial interactions among
patches that are crucial to pathological diagnosis. Recent advancements, such
as Transformer based MIL (TransMIL), have incorporated spatial context and
inter-patch relationships. However, it remains unclear whether explicitly
modeling patch relationships yields similar performance gains in ABMIL, which
relies solely on Multi-Layer Perceptrons (MLPs). In contrast, TransMIL employs
Transformer-based layers, introducing a fundamental architectural shift at the
cost of substantially increased computational complexity. In this work, we
enhance the ABMIL framework by integrating interaction-aware representations to
address this question. Our proposed model, Global ABMIL (GABMIL), explicitly
captures inter-instance dependencies while preserving computational efficiency.
Experimental results on two publicly available datasets for tumor subtyping in
breast and lung cancers demonstrate that GABMIL achieves up to a 7 percentage
point improvement in AUPRC and a 5 percentage point increase in the Kappa score
over ABMIL, with minimal or no additional computational overhead. These
findings underscore the importance of incorporating patch interactions within
MIL frameworks. Our code is available at
\href{https://github.com/tueimage/GABMIL}{\texttt{GABMIL}}.
