Learning from Limited phenotype-level annotations for promoting multiple instance learning in endoscopic helicobacter pylori infection diagnosis.

Abstract

Helicobacter pylori (H. pylori) infection is a major risk factor for gastric carcinogenesis, requiring holistic endoscopic assessment of distributed mucosal abnormalities. While patient-level diagnostic labels are accessible, obtaining large-scale fine-grained annotations is constrained due to high inter-observer variability and the labor-intensive nature of the process. This naturally formulates the task as a multiple instance learning (MIL) problem. Existing image-level approaches often rely on noisy supervisory signals without modeling sequence context, while standard embedding-based MIL methods suffer from suboptimal feature representations and insufficient integration of H. pylori-specific phenotypes. To address these challenges, we propose PhenoMIL, a two-stage framework emulating clinical hierarchical reasoning. First, the Fine-grained Phenotype-level Semi-Supervised Learning (FPS-SL) stage uses multi-label supervised contrastive learning and prototype-based pseudo-label generation to acquire phenotype-specific knowledge and learn transferable representation from limited instance-level annotations. Subsequently, the Clinically Informed MIL (CIMIL) stage performs holistic bag-level diagnosis using Consensus-aware Feature Modulation (CFM) to exploit latent neighborhood structures, and Phenotype-guided Attention Aggregation (PAA) to align instance weighting with diagnostic priors. Extensive evaluation on a large-scale multi-center dataset (303,910 images from 6388 patients) demonstrates PhenoMIL's superiority in both multi-label phenotype classification and H. pylori infection diagnosis. Notably, in a real-world reader study, PhenoMIL achieved 81.63% sensitivity and 87.84% specificity, outperforming both junior and senior endoscopists.