SGPMIL: Sparse Gaussian Process Multiple Instance Learning

• URL: http://arxiv.org/abs/2507.08711v1
• Date: Fri, 11 Jul 2025 16:10:27 GMT
• Title: SGPMIL: Sparse Gaussian Process Multiple Instance Learning
• Authors: Andreas Lolos, Stergios Christodoulidis, Maria Vakalopoulou, Jose Dolz, Aris Moustakas,
• Abstract summary: Multiple Instance Learning (MIL) offers a natural solution for settings where only coarse, bag-level labels are available.<n>We introduce textbfSGPMIL, a new probabilistic attention-based MIL framework grounded in Sparse Gaussian Processes (SGP)<n>Our approach not only preserves competitive bag-level performance but also significantly improves the quality and interpretability of instance-level predictions under uncertainty.
• Score: 7.549262999465268
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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