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.
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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