Name
Abstract | ||
|---|---|---|
Nowadays, medical image segmentation plays an important role in computer-aided medical diagnosis. To realize effective segmentation, Attention Mechanism (AM) is widely adopted. It can be trained to automatically highlight salient features and integrated into convolution neural networks conveniently. However, many researchers choose the attention mechanism without sufficient theoretical interpretability. They ignore the differences and dominant characteristics between various datasets, which causes the failure to select the most appropriate one. In this paper, we explore the implementation and discrimination of four specific attention mechanisms. To evaluate their performances, we incorporate these mechanisms within the U-Net and make a comparison on three medical image datasets. The experimental results show that all these attention mechanisms can improve the value of Mean IoU. More significantly, we find the best AM for each type of dataset and analyze the reasons for different performances from underlying mathematical principles. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-36808-1_65 | NEURAL INFORMATION PROCESSING (ICONIP 2019), PT IV |
Keywords | DocType | Volume |
Deep learning, Attention mechanism, Medical image segmentation | Conference | 1142 |
ISSN | Citations | PageRank |
1865-0929 | 0 | 0.34 |
References | Authors | |
0 | 5 | |