TY - GEN
T1 - R2L-Net
T2 - 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
AU - Wang, Yixin
AU - Yu, Wenxin
AU - Zhang, Zhiqiang
AU - Gong, Jun
AU - Chen, Peng
AU - Liu, Chang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Medical image segmentation plays a crucial role in medical imaging, especially with advancements in techniques like magnetic resonance imaging (MRI) and computed tomography (CT). UNet, a widely used architecture, has shown promising results in medical image segmentation. Several variants based on UNet, and Transformer-based models, like TransUNet, have also exhibited potential for improving segmentation performance. However, these models often require substantial data and computational resources, making them less suitable for on-the-fly segmentation in medical scenarios. This paper proposes a fast medical image segmentation network called R2L-Net, which leverages self-supervised and supervised learning. R2L-Net introduces a self-supervised relative localization task as a regularization term during network training to enhance performance. Compared to UNeXt, our proposed R2L-Net achieves superior results on two public datasets (ISIC and BUSI), with an improved Intersection over Union (IoU) by 5.22 and 5.36, respectively. Moreover, R2L-Net offers several advantages over existing models, including a small number of parameters, low computational complexity, and fast image processing.
AB - Medical image segmentation plays a crucial role in medical imaging, especially with advancements in techniques like magnetic resonance imaging (MRI) and computed tomography (CT). UNet, a widely used architecture, has shown promising results in medical image segmentation. Several variants based on UNet, and Transformer-based models, like TransUNet, have also exhibited potential for improving segmentation performance. However, these models often require substantial data and computational resources, making them less suitable for on-the-fly segmentation in medical scenarios. This paper proposes a fast medical image segmentation network called R2L-Net, which leverages self-supervised and supervised learning. R2L-Net introduces a self-supervised relative localization task as a regularization term during network training to enhance performance. Compared to UNeXt, our proposed R2L-Net achieves superior results on two public datasets (ISIC and BUSI), with an improved Intersection over Union (IoU) by 5.22 and 5.36, respectively. Moreover, R2L-Net offers several advantages over existing models, including a small number of parameters, low computational complexity, and fast image processing.
KW - bedside device
KW - medical image segmentation
KW - rapid
KW - self-supervised
UR - http://www.scopus.com/inward/record.url?scp=85184900630&partnerID=8YFLogxK
U2 - 10.1109/BIBM58861.2023.10385886
DO - 10.1109/BIBM58861.2023.10385886
M3 - Conference contribution
AN - SCOPUS:85184900630
T3 - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
SP - 660
EP - 665
BT - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
A2 - Jiang, Xingpeng
A2 - Wang, Haiying
A2 - Alhajj, Reda
A2 - Hu, Xiaohua
A2 - Engel, Felix
A2 - Mahmud, Mufti
A2 - Pisanti, Nadia
A2 - Cui, Xuefeng
A2 - Song, Hong
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 December 2023 through 8 December 2023
ER -