文献类型：期刊文献

英文题名：Enhancing Hippocampus Segmentation: SwinUNETR Model Optimization with CPS

作者：Cheng, Wangang[1]; He, Guanghua[1]; Zhu, Hancan[1,2]

机构：[1] School of Mathematics, Physics and Information, Shaoxing University, Zhejiang, Shaoxing, 312000, China; [2] Institute of Artificial Intelligence, Shaoxing University, Zhejiang, Shaoxing, 312000, China

年份：2025

卷号：15044 LNCS

起止页码：76

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20244717397692)

语种：英文

外文关键词：Adversarial machine learning - Contrastive Learning - Deep learning - Federated learning - Image segmentation - Medical image processing - Transfer learning

外文摘要：Deep learning techniques have made remarkable strides in medical image segmentation, overcoming many challenges associated with traditional methods. Despite their success, these techniques typically rely on large amounts of manually annotated data, which is both costly and requires expert knowledge for accurate annotations. Additionally, the need for substantial computational power, especially when processing three-dimensional images, further complicates their application. To address these challenges, this paper presents a novel optimization method called Combining Parallel and Sequential Strategy (CPS). This method leverages an efficient parameter transfer learning strategy that integrates the strengths of LoRA and Adapter. CPS can retain the original knowledge structure of the pre-trained model while updating only a minimal number of parameters, thereby reducing the risk of overfitting. We employ CPS to enhance the state-of-the-art SwinUNETR model for medical image segmentation, initially pre-trained on the BraTs2021 dataset, this enhanced model is subsequently applied to three hippocampal datasets. The results reveal that CPS significantly outperforms existing methods, increasing the Dice coefficient by an average of 1.14% and decreasing the HD95 by an average of 0.767, compared to the LoRA method. These findings highlight the effectiveness of our fine-tuning method in leveraging limited data resources, marking a significant advancement in the field of hippocampus segmentation. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.