Mutual Evidential Deep Learning for Medical Image Segmentation

• URL: http://arxiv.org/abs/2505.12418v1
• Date: Sun, 18 May 2025 13:42:27 GMT
• Title: Mutual Evidential Deep Learning for Medical Image Segmentation
• Authors: Yuanpeng He, Yali Bi, Lijian Li, Chi-Man Pun, Wenpin Jiao, Zhi Jin,
• Abstract summary: We propose a mutual evidential deep learning framework that offers a potentially viable solution for pseudo-label generation in semi-supervised learning.<n>We show that MEDL achieves state-of-the-art performance in experiments on five mainstream datasets.
• Score: 39.930548790471896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing semi-supervised medical segmentation co-learning frameworks have realized that model performance can be diminished by the biases in model recognition caused by low-quality pseudo-labels. Due to the averaging nature of their pseudo-label integration strategy, they fail to explore the reliability of pseudo-labels from different sources. In this paper, we propose a mutual evidential deep learning (MEDL) framework that offers a potentially viable solution for pseudo-label generation in semi-supervised learning from two perspectives. First, we introduce networks with different architectures to generate complementary evidence for unlabeled samples and adopt an improved class-aware evidential fusion to guide the confident synthesis of evidential predictions sourced from diverse architectural networks. Second, utilizing the uncertainty in the fused evidence, we design an asymptotic Fisher information-based evidential learning strategy. This strategy enables the model to initially focus on unlabeled samples with more reliable pseudo-labels, gradually shifting attention to samples with lower-quality pseudo-labels while avoiding over-penalization of mislabeled classes in high data uncertainty samples. Additionally, for labeled data, we continue to adopt an uncertainty-driven asymptotic learning strategy, gradually guiding the model to focus on challenging voxels. Extensive experiments on five mainstream datasets have demonstrated that MEDL achieves state-of-the-art performance.

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