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Enhancing U-Net with Spatial-Channel Attention Gate for Abnormal Tissue Segmentation in Medical Imaging
| Content Provider | MDPI |
|---|---|
| Author | Khanh, Trinh Dao, Duy-Phuong Ho, Ngoc-Huynh Yang, Hyung-Jeong Baek, Eu-Tteum Lee, Gueesang Kim, Soo-Hyung Yoo, Seok |
| Copyright Year | 2020 |
| Description | In recent years, deep learning has dominated medical image segmentation. Encoder-decoder architectures, such as U-Net, can be used in state-of-the-art models with powerful designs that are achieved by implementing skip connections that propagate local information from an encoder path to a decoder path to retrieve detailed spatial information lost by pooling operations. Despite their effectiveness for segmentation, these naïve skip connections still have some disadvantages. First, multi-scale skip connections tend to use unnecessary information and computational sources, where likable low-level encoder features are repeatedly used at multiple scales. Second, the contextual information of the low-level encoder feature is insufficient, leading to poor performance for pixel-wise recognition when concatenating with the corresponding high-level decoder feature. In this study, we propose a novel spatial-channel attention gate that addresses the limitations of plain skip connections. This can be easily integrated into an encoder-decoder network to effectively improve the performance of the image segmentation task. Comprehensive results reveal that our spatial-channel attention gate remarkably enhances the segmentation capability of the U-Net architecture with a minimal computational overhead added. The experimental results show that our proposed method outperforms the conventional deep networks in term of Dice score, which achieves 71.72%. |
| Starting Page | 5729 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app10175729 |
| Journal | Applied Sciences |
| Issue Number | 17 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-08-19 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Artificial Intelligence Medical Image Segmentation Semantic Segmentation U-net Attention Gates |
| Content Type | Text |
| Resource Type | Article |
