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

Taymaz Akan¹, Richa Aishwarya², Md Shenuarin Bhuiyan²

¹Department of Medicine, LSU Health Shreveport, Shreveport, LA, USA.

|July 16, 2026

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Computational pathology with dynamic convolutional and adaptive kernels.

Journal of pathology informatics·2026

Self-supervised learning (SSL) models like SSL-HistoNet can identify disease-specific tissue structures from unannotated histopathological images, reducing manual labeling needs. This approach shows high performance, comparable to supervised methods, for analyzing muscle pathologies.

Area of Science:

Computational pathology
Machine learning in histopathology
Digital pathology advancements

Background:

Pathology decision support systems require large, manually annotated datasets, hindering clinical implementation.
Self-supervised learning (SSL) offers a solution by extracting features from unannotated images.

Purpose of the Study:

Introduce SSL-HistoNet, a novel model for learning disease-relevant morphological representations from histopathological images using SSL.
Demonstrate the model's capability in an annotation-free setting.

Main Methods:

Applied SSL-HistoNet to WGA-stained skeletal muscle tissues from mouse models of amyotrophic lateral sclerosis (ALS) and Type I diabetes.
Pretrained the SSL encoder on unlabeled data, then integrated it with an attention-guided classifier to detect pathological alterations.

Keywords:

Computational pathology Contrastive learning Self-supervised learning Skeletal muscle WGA staining

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Main Results:

SSL-HistoNet achieved high performance with 0.98 precision, 0.98 recall, and 0.98 AUC, outperforming state-of-the-art supervised models.
Feature analyses revealed consistent, class-level changes in morphology-related patterns.

Conclusions:

SSL-HistoNet provides an annotation-free framework for identifying disease-specific tissue structures.
The model reduces manual labeling demands and inter-/intra-observer variability in histology.