Classification and segmentation are crucial in medical image analysis as they enable accurate diagnosis and disease monitoring. However, current methods often prioritize the mutual learning features and shared model parameters, while neglecting the reliability of features and performances. In this paper, we propose a novel Uncertainty-informed Mutual Learning (UML) framework for reliable and interpretable medical image analysis. Our UML introduces reliability to joint classification and segmentation tasks, leveraging mutual learning with uncertainty to improve performance. To achieve this, we first use evidential deep learning to provide image-level and pixel-wise confidences. Then, an Uncertainty Navigator Decoder is constructed for better using mutual features and generating segmentation results. Besides, an Uncertainty Instructor is proposed to screen reliable masks for classification. Overall, UML could produce confidence estimation in features and performance for each link (classification and segmentation). The experiments on the public datasets demonstrate that our UML outperforms existing methods in terms of both accuracy and robustness. Our UML has the potential to explore the development of more reliable and explainable medical image analysis models. We will release the codes for reproduction after acceptance.

翻译：分类与分割是医学图像分析中的关键任务，能够实现精确诊断与疾病监测。然而现有方法往往过度关注互学习特征与共享模型参数，忽视了特征及性能的可靠性。本文提出一种新颖的不确定性信息引导的互学习（UML）框架，用于可靠且可解释的医学图像分析。该方法将可靠性引入联合分类与分割任务，通过结合不确定性的互学习机制提升性能。具体实现时，首先采用证据深度学习提供图像级与像素级置信度；进而构建不确定性导航解码器，以更有效利用互特征并生成分割结果；同时设计不确定性指导器，为分类任务筛选可靠掩膜。整体而言，UML可为每个环节（分类与分割）提供特征与性能的置信度估计。在公开数据集上的实验表明，本方法在精确度与鲁棒性方面均优于现有方法。UML有望推动更可靠、可解释的医学图像分析模型发展。代码将在论文接收后开源。