标题： 基于信息量的共证据融合在医学图像分割中的应用 显示英文标题

标题： Co-Evidential Fusion with Information Volume for Medical Image Segmentation

摘要： 尽管现有的半监督图像分割方法已取得了良好的性能，但它们无法有效利用多源体素级不确定性来进行针对性学习。 因此，我们提出了两项主要改进。 首先，我们引入了一种新颖的信念共证据融合策略，该策略基于传统D-S证据理论扩展的一般化证据深度学习方法，以获得医学样本中每个体素更精确的不确定性度量。 这有助于模型学习混合标注信息，并在标注数据和未标注数据之间建立语义关联。 其次，我们引入了质量函数的信息量（IVUM）的概念来评估构建的证据，实现了两种证据学习方案。 一种通过结合质量函数的信息量与原始不确定性度量来优化证据深度学习。 另一种结合基于共证据融合策略的学习模式，利用IVUM设计新的优化目标。 在四个数据集上的实验表明了我们方法的竞争性性能。 显示英文摘要

摘要： Although existing semi-supervised image segmentation methods have achieved good performance, they cannot effectively utilize multiple sources of voxel-level uncertainty for targeted learning. Therefore, we propose two main improvements. First, we introduce a novel pignistic co-evidential fusion strategy using generalized evidential deep learning, extended by traditional D-S evidence theory, to obtain a more precise uncertainty measure for each voxel in medical samples. This assists the model in learning mixed labeled information and establishing semantic associations between labeled and unlabeled data. Second, we introduce the concept of information volume of mass function (IVUM) to evaluate the constructed evidence, implementing two evidential learning schemes. One optimizes evidential deep learning by combining the information volume of the mass function with original uncertainty measures. The other integrates the learning pattern based on the co-evidential fusion strategy, using IVUM to design a new optimization objective. Experiments on four datasets demonstrate the competitive performance of our method.