用证据融合提升医学图像分割的不确定性建模能力
Co-Evidential Fusion with Information Volume for Medical Image Segmentation
- 提出新型共证据融合策略，结合证据理论优化体素级不确定度
- 引入信息体积衡量证据强度，提升模型对标注与未标注数据的关联学习
- 适用于医学图像分割中弱监督场景，尤其适合标注稀缺任务
现有半监督图像分割方法虽表现良好，但难以有效利用多源体素级不确定性进行针对性学习。为此，本文提出两项改进：首先，基于广义证据深度学习，扩展传统D-S证据理论，提出新颖的贝叶斯共证据融合策略，实现更精确的体素级不确定性度量，帮助模型融合混合标注信息，并建立标注与未标注数据间的语义关联；其次，引入质量函数的信息体积（IVUM）概念评估构建的证据，设计两种证据学习方案：其一将IVUM与原始不确定度结合，优化证据深度学习；其二基于共证据融合策略，利用IVUM设计新优化目标。在四个数据集上的实验表明，该方法具有竞争力。
原文摘要 · Abstract (English)
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.
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