Mutual Contrastive Learning to Disentangle Whole Slide Image Representations for Glioma Grading

Whole slide images (WSI) provide valuable phenotypic information for histological assessment and malignancy grading of tumors. The WSI-based computational pathology promises to provide rapid diagnostic support and facilitate digital health. The most commonly used WSI are derived from formalin-fixed paraffin-embedded (FFPE) and frozen sections. Currently, the majority of automatic tumor grading models are developed based on FFPE sections, which could be affected by the artifacts introduced by tissue processing. Here we propose a mutual contrastive learning scheme to integrate FFPE and frozen sections and disentangle cross-modality representations for glioma grading. We first design a mutual learning scheme to jointly optimize the model training based on FFPE and frozen sections. Further, we develop a multi-modality domain alignment mechanism to ensure semantic consistency in the backbone model training. We finally design a sphere normalized temperature-scaled cross-entropy loss (NT-Xent), which could promote cross-modality representation disentangling of FFPE and frozen sections. Our experiments show that the proposed scheme achieves better performance than the model trained based on each single modality or mixed modalities. The sphere NT-Xent loss outperforms other typical metrics loss functions.