Recruiting Teacher IF Modality for Nephropathy Diagnosis: A Customized Distillation Method With Attention-Based Diffusion Network

The joint use of multiple modalities for medical image processing has been widely studied in recent years. The fusion of information from different modalities has demonstrated the performance improvement for a lot of medical tasks. For nephropathy diagnosis, immunofluorescence (IF) is one of the most widely-used multi-modality medical images due to its ease of acquisition and the effectiveness for certain nephropathy. However, the existing methods mainly assume different modalities have the equal effect on the diagnosis task, failing to exploit multi-modality knowledge in details. To avoid this disadvantage, this paper proposes a novel customized multi-teacher knowledge distillation framework to transfer knowledge from the trained single-modality teacher networks to a multi-modality student network. Specifically, a new attention-based diffusion network is developed for IF based diagnosis, considering global, local, and modality attention. Besides, a teacher recruitment module and diffusion-aware distillation loss are developed to learn to select the effective teacher networks based on the medical priors of the input IF sequence. The experimental results in the test and external datasets show that the proposed method has a better nephropathy diagnosis performance and generalizability, in comparison with the state-of-the-art methods.