基于三重交互关注网络的医学图像分割算法

doi:10.16451/j.cnki.issn1003-6059.202105002

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Abstract Deep learning produces advantages in solving class imbalance due to its powerful ability to extract features. However, its segmentation accuracy and efficiency can still be improved. A medical image segmentation algorithm via triplet interactive attention network is proposed in this paper. A triplet interactive attention module is designed and embedded into the feature extraction process. The module is focused on features in the channel and spatial dimensions jointly, capturing cross-dimensional interactive information. Thus, important features are in focus and target locations are highlighted. Moreover, pixel position-aware loss is employed to further mitigate the impact of class imbalance. Experiments on medical image datasets show that the proposed method yields better performance.

Key words： Deep Learning Semantic Segmentation Class Imbalance Attention Mechanism

Received: 21 February 2021

ZTFLH:

TP 391

Fund:National Natural Science Foundation of China(No.62006215), Natural Science Foundation of Zhejiang Province(No.LZ20F030001)

Corresponding Authors: CAO Feilong, Ph.D., professor. His research interests include deep learning and image processing.

About author:: GAO Chengling, master student. Her research interests include deep learning and image processing.YE Hailiang, Ph.D., lecturer. His research interests include deep learning and image processing.

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Cite this article:

GAO Chengling,YE Hailiang,CAO Feilong. Medical Image Segmentation via Triplet Interactive Attention Network[J]. , 2021, 34(5): 398-406.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202105002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I5/398

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