Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation Learning
This work addresses representation learning for medical imaging, specifically histopathology, by integrating language modality, offering improved interpretability and performance.
The paper tackles the problem of learning rich representations for histopathology images by combining hierarchical visual information with domain-specific natural language descriptions, resulting in state-of-the-art performance on OpenSRH and TCGA benchmarks.
Self-supervised representation learning has been highly promising for histopathology image analysis with numerous approaches leveraging their patient-slide-patch hierarchy to learn better representations. In this paper, we explore how the combination of domain specific natural language information with such hierarchical visual representations can benefit rich representation learning for medical image tasks. Building on automated language description generation for features visible in histopathology images, we present a novel language-tied self-supervised learning framework, Hierarchical Language-tied Self-Supervision (HLSS) for histopathology images. We explore contrastive objectives and granular language description based text alignment at multiple hierarchies to inject language modality information into the visual representations. Our resulting model achieves state-of-the-art performance on two medical imaging benchmarks, OpenSRH and TCGA datasets. Our framework also provides better interpretability with our language aligned representation space. Code is available at https://github.com/Hasindri/HLSS.