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

Mai Xu;Ning Dai;Lai Jiang;Yibing Fu;Xin Deng;Shengxi Li
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Abstract

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.
肾病诊断的教师IF招募模式:基于注意力扩散网络的定制精馏方法
近年来,多种模式联合应用于医学图像处理得到了广泛的研究。不同模式的信息融合已经证明了许多医疗任务的性能提高。对于肾病的诊断,免疫荧光(IF)由于其易于获取和对某些肾病的有效性而成为应用最广泛的多模态医学图像之一。然而,现有的方法主要假设不同模态对诊断任务的作用是相等的,未能详细地利用多模态知识。为了避免这一缺点,本文提出了一种新的定制的多教师知识蒸馏框架,将知识从训练有素的单模态教师网络转移到多模态学生网络。具体来说,我们开发了一个新的基于注意力的扩散网络,用于基于IF的诊断,考虑了全局、局部和模态注意力。此外,开发了教师招聘模块和扩散感知蒸馏损失,学习基于输入中频序列的医学先验选择有效的教师网络。测试和外部数据集的实验结果表明,与现有方法相比,该方法具有更好的肾病诊断性能和通用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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