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

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 pseudo-labeling for semantic segmentation tasks. In an evaluation on a large international dataset (multi-class tissue segmentation algorithm for colorectal domain), our approach outperforms traditional supervised and semi-supervised strategies by improving the Dice score by 3.6% for tumor segmentation and 2.1% for tumor/tumor stroma segmentation. Ablation studies performed with different numbers of annotated and unannotated WSIs, as well as training in a monocentric training scenario, further confirm the robustness and superior performance of the proposed S-o-M framework. These findings highlight the value of incorporating case-to-case similarities into SSL strategies to build more effective and general computational pathology models.