MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation
By Tongle Fan, Guanglei Wang, Yan Li, Hongrui Wang. IEEE Access, 2020
427 citations recorded by the source corpus.
Automatic assessing the location and extent of liver and liver tumor is critical for radiologists, diagnosis and the clinical process. In recent years, a large number of variants of U-Net based on Multi-scale feature fusion are proposed to improve the segmentation performance for medical image segmentation. Unlike the previous works which extract the context information of medical image via applying the multi-scale feature fusion, we propose a novel network named Multi-scale Attention Net (MA-Net) by introducing self-attention mechanism into our method to adaptively integrate local features with their global dependencies. The MA-Net can capture rich contextual dependencies based on the attention mechanism. We design two blocks: Position-wise Attention Block (PAB) and Multi-scale Fusion Attention Block (MFAB). The PAB is used to model the feature interdependencies in spatial dimensions, which capture the spatial dependencies between pixels in a global view. In addition, the MFAB is to capture the channel dependencies between any feature map by multi-scale semantic feature fusion. We evaluate our method on the dataset of MICCAI 2017 LiTS Challenge. The proposed method achieves better performance than other state-of-the-art methods. The Dice values of liver and tumors segmentation are 0.960 ± 0.03 and 0.749 ± 0.08 respectively.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/28d68a63b95ae3032bbef8881eee4a74e8731a4b
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- Ehsan Khodapanah Aghdam
- Dorit Merhof
- Reza Azad
- Joseph Paul Cohen
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- Yiwei Jia
Citations in the corpus
- Cellpose3: one-click image restoration for improved cellular segmentation (2024, neutral): “This transformer consisted of a transformer-based seg-former encoder and a multi-scale attention decoder, which was trained to predict the cell flows and probabilities from Cellpose [56, 57].”
- Medical Image Segmentation Review: The Success of U-Net (2022, methodology): “BE ASPP [81] MA-Net [76] COPLE-Net [82] SA-UNet [77] FRCU-Net [79] JCS [83] MS-Net [129] The network bottleneck contains the compressed representation of the input data and provides necessary information (e.”