Co-Evidential Fusion with Information Volume for Medical Image Segmentation
This is an incremental improvement for medical image segmentation, addressing uncertainty utilization in semi-supervised learning.
The paper tackled the problem of semi-supervised medical image segmentation by proposing a co-evidential fusion strategy and information volume concept to better utilize voxel-level uncertainty, resulting in competitive performance on four datasets.
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.