From unlabeled to labeled: self-supervised deep learning in computational pathology

The advancement of decision support systems for pathology and their implementation in clinical practice have been limited by the necessity for extensive, manually annotated datasets. Self-supervised learning (SSL) automates the extraction and interpretation of histopathological features from unannotated images, facilitating efficient model development without dependence on expert labeling. In this study, we introduce the SSL-HistoNet model that learns disease-relevant morphological representations from histopathological images through self-supervised learning.

We applied it to WGA-stained skeletal muscle tissues from mouse models of amyotrophic lateral sclerosis (ALS) and Type I diabetes to explore its ability to capture pathological muscle phenotypes in an annotation-free setting. Following pretraining on unlabeled data, the SSL encoder was further integrated with an attention-guided classifier to evaluate its capacity to identify pathological muscle alterations.

SSL-HistoNet achieved a precision of 0.98, a recall of 0.98, and an AUC of 0.98, demonstrating performance comparable to or outperforming state-of-the-art supervised models. Alongside high discriminative performance, exploratory feature analyses demonstrated consistent class-level changes in morphology-related patterns identified through representation learning.

These findings highlight the capability of SSL-HistoNet as an annotation-free framework for outlining disease-specific tissue structures, reducing manual labeling demands and mitigating inter- and intra-observer variability in histological processes.

Not applicable.