CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network
2022
Online
report
U-Net and its extensions have achieved great success in medical image segmentation. However, due to the inherent local characteristics of ordinary convolution operations, U-Net encoder cannot effectively extract global context information. In addition, simple skip connections cannot capture salient features. In this work, we propose a fully convolutional segmentation network (CMU-Net) which incorporates hybrid convolutions and multi-scale attention gate. The ConvMixer module extracts global context information by mixing features at distant spatial locations. Moreover, the multi-scale attention gate emphasizes valuable features and achieves efficient skip connections. We evaluate the proposed method using both breast ultrasound datasets and a thyroid ultrasound image dataset; and CMU-Net achieves average Intersection over Union (IoU) values of 73.27% and 84.75%, and F1 scores of 84.81% and 91.71%. The code is available at https://github.com/FengheTan9/CMU-Net.
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CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network
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Autor/in / Beteiligte Person: | Tang, Fenghe ; Wang, Lingtao ; Ning, Chunping ; Xian, Min ; Ding, Jianrui |
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Veröffentlichung: | 2022 |
Medientyp: | report |
DOI: | 10.1109/ISBI53787.2023.10230609 |
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