HistoPerm: A permutation-based view generation approach for improving histopathologic feature representation...

Area of Science:

• Digital Pathology
• Computational Biology
• Machine Learning

Background:

• Deep learning excels in digital pathology but requires extensive labeled data.
• Acquiring large labeled datasets for histology image analysis is resource-intensive.

Purpose of the Study:

• To introduce HistoPerm, a novel view generation method for representation learning in histology images.
• To enhance deep learning model performance using joint embedding architectures with limited labeled data.

Main Methods:

• HistoPerm permutes augmented views of whole-slide histology image patches.
• Evaluated on Celiac disease and Renal Cell Carcinoma datasets using BYOL, SimCLR, and VICReg.
• Compared performance against fully supervised baseline models.

Main Results:

• HistoPerm consistently improved patch- and slide-level classification accuracy, F1-score, and AUC.
• Significant accuracy boosts observed across BYOL, SimCLR, and VICReg models on both datasets.
• HistoPerm-enhanced models achieved performance comparable or superior to fully supervised methods.

Conclusions:

• HistoPerm effectively improves representation learning for histopathology features with limited labeled data.
• The method offers a valuable tool for enhancing digital pathology analysis.
• HistoPerm facilitates achieving high classification performance without extensive manual labeling.