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，能够实现精准诊断和疾病监测。然而当前方法往往优先考虑互学习特征和共享模型参数，却忽略了特征与性能的可靠性。本文提出一种新型不确定性感知互学习（UML）框架，用于可靠且可解释的医学图像分析。该框架将可靠性引入联合分类与分割任务，通过不确定性驱动的互学习提升性能。具体而言，我们首先利用证据深度学习获取图像级和像素级置信度。随后构建不确定性导航解码器，以更优方式利用互特征生成分割结果。此外，还提出不确定性指导模块用于筛选分类任务的可靠掩膜。整体上，UML能够为每个环节（分类与分割）提供特征与性能的置信度评估。在公开数据集上的实验表明，本方法在准确性和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。论文接收后将开源代码以供复现。