Hospital-Agnostic Image Representation Learning in Digital Pathology.

Area of Science:

• Digital Pathology
• Machine Learning
• Computer Vision

Background:

• Whole Slide Images (WSIs) are crucial for cancer subtype diagnosis.
• Variability in WSI acquisition across trial sites (e.g., scanners, staining) introduces domain shift.
• This domain shift challenges consistent diagnosis using machine learning models.

Purpose of the Study:

• To improve the generalization capability of Deep Neural Networks (DNNs) for histopathology image analysis.
• To address the domain shift problem in digital pathology datasets from different hospitals.
• To develop a hospital-agnostic learning approach for robust cancer diagnosis.

Main Methods:

• Leveraged domain generalization techniques to train a DNN.
• Applied a proposed hospital-agnostic learning strategy.
• Visualized low-dimensional latent space representations.
• Evaluated classification accuracy on unseen histopathology image sets.

Main Results:

• Conventional supervised learning exhibited poor generalization to data from different hospitals.
• The proposed hospital-agnostic learning approach significantly improved generalization.
• Low-dimensional latent space visualization supported the improved generalization.
• Classification accuracy was enhanced on diverse datasets.

Conclusions:

• Domain generalization is effective in overcoming domain shift in digital pathology.
• Hospital-agnostic learning enhances the robustness of DNNs for cancer diagnosis.
• The developed method enables consistent diagnostic performance across different healthcare settings.