Uni-Dual: A Generic Unified Dual-Task Medical Self-Supervised Learning Framework

RGB images and medical hyperspectral images (MHSIs) are two widely-used modalities in computational pathology. The former is cheap, easy and fast to obtain while lacking pathological information such as physiochemical state. The latter is an emerging modality which captures electromagnetic radiation-matter interaction but suffers from problems such as high time cost and low spatial resolution. In this paper, we bring forward a unified dual-task multi-modality self-supervised learning (SSL) framework, called Uni-Dual, which takes the most use of both paired and unpaired RGB-MHSIs. Concretely, we design a unified SSL paradigm for RGB images and MHSIs. Two tasks are proposed: (1) a discrimination learning task which learns high-level semantics via mining the cross-correlation across unpaired RGB-MHSIs, (2) a reconstruction learning task which models low-level stochastic variations via furthering the interaction across RGB-MHSI pairs. Our Uni-Dual enjoys the following benefits: (1) A unified model which can be easily transferred to different downstream tasks on various modality combinations. (2) We consider multi-constituent and structured information learning from MHSIs and RGB images for low-cost high-precision clinical purposes. Experiments conducted on various downstream tasks with different modalities show the proposed Uni-Dual substantially outperforms other competitive SSL methods.