标题： 不确定性感知的多专家知识蒸馏用于疾病分级不平衡问题 显示英文标题

标题： Uncertainty-Aware Multi-Expert Knowledge Distillation for Imbalanced Disease Grading

摘要： 自动疾病图像分级是人工智能在医疗保健领域的一项重要应用，能够更快、更准确地评估患者病情。 然而，由于数据不平衡而加剧的领域转移会给模型带来偏差，从而给临床应用的部署带来困难。 为了解决这个问题，我们提出了一种新颖的 \textbf{U}不确定性感知 \textbf{M}多专家 \textbf{K}知识 \textbf{D}蒸馏 (UMKD) 框架，用于将知识从多个专家模型迁移到单个学生模型。 具体而言，为了提取判别性特征，UMKD 通过在特征空间中进行浅层紧凑的特征对齐，将任务无关特征和任务特定特征解耦。 在输出空间，不确定性感知解耦蒸馏 (UDD) 机制会根据专家模型的不确定性动态调整知识迁移权重，从而确保蒸馏过程的稳健性和可靠性。 此外，UMKD 还解决了模型架构异构性以及源域和目标域之间分布差异的问题，而这些问题是以往的知识图谱 (KD) 方法未能充分解决的。 在前列腺组织学分级 (\textit{SICAPv2}) 和眼底图像分级 (\textit{APTOS}) 上进行的大量实验表明， UMKD 在源域不平衡和目标域不平衡场景下均达到了新的最佳水平，为现实世界的疾病图像分级提供了一个稳健且实用的解决方案。 显示英文摘要

摘要： Automatic disease image grading is a significant application of artificial intelligence for healthcare, enabling faster and more accurate patient assessments. However, domain shifts, which are exacerbated by data imbalance, introduce bias into the model, posing deployment difficulties in clinical applications. To address the problem, we propose a novel \textbf{U}ncertainty-aware \textbf{M}ulti-experts \textbf{K}nowledge \textbf{D}istillation (UMKD) framework to transfer knowledge from multiple expert models to a single student model. Specifically, to extract discriminative features, UMKD decouples task-agnostic and task-specific features with shallow and compact feature alignment in the feature space. At the output space, an uncertainty-aware decoupled distillation (UDD) mechanism dynamically adjusts knowledge transfer weights based on expert model uncertainties, ensuring robust and reliable distillation. Additionally, UMKD also tackles the problems of model architecture heterogeneity and distribution discrepancies between source and target domains, which are inadequately tackled by previous KD approaches. Extensive experiments on histology prostate grading (\textit{SICAPv2}) and fundus image grading (\textit{APTOS}) demonstrate that UMKD achieves a new state-of-the-art in both source-imbalanced and target-imbalanced scenarios, offering a robust and practical solution for real-world disease image grading.