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：分类与分割在医学图像分析中至关重要，因其能够实现精确诊断与疾病监测。然而，现有方法往往优先考虑互学习特征与共享模型参数，却忽视了特征与性能的可靠性。本文提出一种新颖的不确定性感知互学习框架，用于实现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，通过结合不确定性的互学习提升性能。为此，我们首先利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优化互特征利用并生成分割结果。此外，提出不确定性指导模块用于筛选可靠掩膜以辅助分类。总体而言，UML能够为分类与分割各环节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，本方法在准确性与鲁棒性方面均优于现有方法。该模型有望推动更可靠、可解释的医学图像分析模型发展。论文接收后我们将公开代码以供复现。