Robust Semi-Supervised Learning for Histopathology Images Through Self-Supervision Guided Out-of-Distribution Scoring

Semi-supervised learning (semi-SL) offers a promising solution for challenging scenarios in medical image analysis where acquiring sufficient labeled data is difficult. However, practical applications often violate the assumption that the unlabeled data distribution matches the labeled samples, especially in medical imaging. This leads to out-of-distribution (OOD) samples that hinder algorithm efficiency. Filtering methods commonly used for outlier samples may not be suitable for diverse anatomical structures and rare morphologies in medical images. To address these challenges in digital histology images, we propose a novel pipeline. Our approach utilizes self-supervised learning to estimate an OOD score for each unlabeled data point, enabling calibration of the subsequent semi-SL framework. By modulating sample selection based on the outlier score, we prioritize samples aligned with the labeled distribution during the semi-SL stage. Our framework is compatible with any semi-SL approach and has been validated on two digital pathology datasets. Extensive experiments on a colorectal histology dataset and a TCGA-BRCA whole slide image dataset demonstrate the superiority of our method compared to popular semi-SL techniques, particularly the widely used Mixmatch framework. Our approach enhances medical image analysis performance and shows promise in addressing challenges related to open-set supervised learning.