SGPMIL: Sparse Gaussian Process Multiple Instance Learning

Andreas Lolos, Stergios Christodoulidis, Aris L. Moustakas, Jose Dolz, Maria Vakalopoulou

Keywords: Multiple Instance Learning, Probabilistic Attention mechanism, Gaussian Processes, Histopathology

Domains: Vision and Learning, AI for Health

TL;DR: SGPMIL uses Sparse Gaussian Processes for probabilistic attention in whole slide image classification, enabling uncertainty quantification with leading instance-level localization and competitive bag-level accuracy.

External Link: https://openaccess.thecvf.com/content/WACV2026/papers/Lolos_SGPMIL_Sparse_Gaussian_Process_Multiple_Instance_Learning_WACV_2026_paper.pdf

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 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 is available at: https://github.com/mandlos/SGPMIL

Submission Number: 6