التفاصيل البيبلوغرافية
| العنوان: |
Medical Image Segmentation with Dual-Encoding and Multi-Level Feature Adaptive Fusion. |
| المؤلفون: |
Wu, Shulei1 (AUTHOR) 2021110516003@stu.cqnu.edu.cn, Yang, You2 (AUTHOR) 20130958@cqnu.edu.cn, Zhang, Fanghong2 (AUTHOR) 122503944@qq.com |
| المصدر: |
International Journal of Pattern Recognition & Artificial Intelligence. Mar2024, Vol. 38 Issue 4, p1-26. 26p. |
| مصطلحات موضوعية: |
*COMPETITIVE advantage in business, DIAGNOSTIC imaging, IMAGE representation, DIAGNOSIS, PYELONEPHRITIS |
| مستخلص: |
Purpose: Accurate segmentation of medical images is critical for disease diagnosis, surgical planning and prognostic assessment. TransUNet, a hybrid CNN-Transformer-based method, extracts local features using CNN and compensates for the lack of long-range dependencies through a self-attention mechanism. However, the initial focus on extracting local features from specific regions impacts the generation of subsequent global features, thus constraining the model's capacity to effectively capture a broader range of semantic information. Effective integration of local and global features plays a pivotal role in achieving precise and dense prediction. Therefore, we propose a novel hybrid CNN-Transformer-based method aimed at enhancing medical image segmentation. Approach: In this study, a dual-encoder parallel structure is used to enhance the feature representation of the input image. By introducing a multi-scale adaptive feature fusion module, a fine fusion of local features across perceptual domains is realized in the decoding process. The generalized convolutional block attention module helps to increase cross-channel interactions in layers with more channels, thus enabling the fusion of local features and global representations at different resolutions during the decoding process. Results: The proposed method achieves average DSC scores of 79.98%, 84.83% and 85.78% on the Synapse, ISIC2017 and Pediatric Pyelonephritis datasets, respectively. These scores are 2.5%, 0.56% and 0.42% higher than those of TransUNet. The best performance of 91.66% is observed on the ACDC dataset, representing improvements of 2.46% and 7.24% compared to HiFormer and DAE-Former, respectively. Conclusions: The experimental results show that the proposed model has a significant competitive advantage in terms of ACDC image segmentation performance. [ABSTRACT FROM AUTHOR] |
|
Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Business Source Index |