Name
Title | ||
|---|---|---|
Multi-scale Generative Adversarial Network for Automatic Sublingual Vein Segmentation. |
Abstract | ||
|---|---|---|
Sublingual vein segmentation is an essential yet challenging task in computer-aided Traditional Chinese Medicine (TCM) tongue diagnosis. The most intricate part of sublingual vein segmentation is the strong diversity of sublingual vein images (e.g., various exposure of vein and complex background) and the natural connectivity of veins. To this end, we propose a novel and effective approach based on generative adversarial networks (GANs). Specifically, the framework comprises two modules: a segmentation generator network with multi-scale outputs and a scale-consistent discriminator. The former segmentation generator learns the overall structure of the segmentation, which reduces the training difficulties of stride 1 output by multi-scale outputs. The latter scale-consistent discriminator regularizes the segmentation maps from different output strides, which keeps the natural connectivity of veins and avoids generated noise. Additionally, we have constructed and publicized a well-annotated sublingual vein dataset (FDU-SV) which we believe will promote the significant development of this area. Extensive experimental results confirm that our method outperforms other representative segmentation models with a remarkable margin, achieving the state-of-the-art performance of 64.53% mIoU score. Code and dataset are available at https://github.com/echobear313/FDUVEIN. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BIBM49941.2020.9313105 | BIBM |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingyue Xiong | 1 | 0 | 0.34 |
| Xinlei Li | 2 | 0 | 3.04 |
| Yang Dawei | 3 | 2 | 4.20 |
| Wei Zhang | 4 | 0 | 1.01 |
| Ye Zhang | 5 | 0 | 1.01 |
| Yajie Kong | 6 | 0 | 1.01 |
| Fufeng Li | 7 | 0 | 0.68 |
| Wenqiang Zhang | 8 | 5 | 6.50 |