ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2023
[A survey of loss function of medical image segmentation algorithms].
Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Intelligent data governance and quality control for chest Bi syndrome/coronary heart disease across the prevention-treatment-rehabilitation continuum: integrating a standardized framework, adversarially-optimized trigger engine, and domain-adaptive AI.Frontiers in medical technology · 2026Article
- Towards Reliable Healthcare Imaging: A Multifaceted Approach in Class Imbalance Handling for Medical Image Segmentation.Interdisciplinary sciences, computational life sciences · 2025Article
- [Thyroid nodule segmentation method integrating receiving weighted key-value architecture and spherical geometric features].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- [Multi-scale medical image segmentation based on pixel encoding and spatial attention mechanism].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
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8 authors.
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Abstract
Medical image segmentation based on deep learning has become a powerful tool in the field of medical image processing. Due to the special nature of medical images, image segmentation algorithms based on deep learning face problems such as sample imbalance, edge blur, false positive, false negative, etc. In view of these problems, researchers mostly improve the network structure, but rarely improve from the unstructured aspect. The loss function is an important part of the segmentation method based on deep learning. The improvement of the loss function can improve the segmentation effect of the network from the root, and the loss function is independent of the network structure, which can be used in various network models and segmentation tasks in plug and play. Starting from the difficulties in medical image segmentation, this paper first introduces the loss function and improvement strategies to solve the problems of sample imbalance, edge blur, false positive and false negative. Then the difficulties encountered in the improvement of the current loss function are analyzed. Finally, the future research directions are prospected. This paper provides a reference for the reasonable selection, improvement or innovation of loss function, and guides the direction for the follow-up research of loss function.
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