SGPMIL: Sparse Gaussian Process Multiple Instance Learning
Journal:
arXiv
Published Date:
Jul 11, 2025
Abstract
Multiple Instance Learning (MIL) offers a natural solution for settings where
only coarse, bag-level labels are available, without having access to
instance-level annotations. This is usually the case in digital pathology,
which consists of gigapixel sized images. While deterministic attention-based
MIL approaches achieve strong bag-level performance, they often overlook the
uncertainty inherent in instance relevance. In this paper, we address the lack
of uncertainty quantification in instance-level attention scores by introducing
\textbf{SGPMIL}, a new probabilistic attention-based MIL framework grounded in
Sparse Gaussian Processes (SGP). By learning a posterior distribution over
attention scores, SGPMIL enables principled uncertainty estimation, resulting
in more reliable and calibrated instance relevance maps. Our approach not only
preserves competitive bag-level performance but also significantly improves the
quality and interpretability of instance-level predictions under uncertainty.
SGPMIL extends prior work by introducing feature scaling in the SGP predictive
mean function, leading to faster training, improved efficiency, and enhanced
instance-level performance. Extensive experiments on multiple well-established
digital pathology datasets highlight the effectiveness of our approach across
both bag- and instance-level evaluations. Our code will be made publicly
available.
