Similarity-guided swarm of models: enhancing semi-supervised learning in computational pathology.

ABSTRACT: High-precision pixel-level annotation has been a major bottleneck in computational pathology due to its time-consuming nature and reliance on expert knowledge. Semi-supervised learning (SSL) provides a promising approach to alleviate this challenge by leveraging large amounts of unlabeled data. However, existing pseudo-labeling-based SSL methods often overlook intrinsic properties, such as inter-case similarities, which are critical for generating accurate pseudo-labels in complex tissue environments. In this study, we propose a Swarm-of-Models (S-o-M) SSL framework that dynamically selects "morphology expert" models (i.e., models specialized in recognizing specific tissue structures) for each unlabeled whole-slide image (WSI) based on similarity, thereby improving the reliability of pseud

SUBMITTER: Weng Z 

PROVIDER: S-EPMC12753758 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature