Name
Title | ||
|---|---|---|
Deep co-supervision and attention fusion strategy for automatic COVID-19 lung infection segmentation on CT images |
Abstract | ||
|---|---|---|
•An ESM is presented to highlight low-level boundary features, and the edge supervised information is incorporated into the initial stage of down-sampling.•An ASSM is proposed to enhance high-level semantics from feature maps with different scales, and the mask supervised information is introduced into the later stage of down-sampling.•An AFM is developed to fuse various scale feature maps from the up-sampling stage. An attention mechanism is utilized to reduce the semantic gaps between high-level and low-level feature maps, so as to strengthen and supplement the lost detailed information in high-level representations.•A joint loss function is constructed by combining the edge supervised loss, auxiliary semantic supervised loss and fusion loss, thereby achieving a deep collaborative supervision on edges and semantics. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patcog.2021.108452 | Pattern Recognition |
Keywords | DocType | Volume |
Semantic segmentation,Multi-scale features,Attention mechanism,Feature fusion,COVID-19 | Journal | 124 |
ISSN | Citations | PageRank |
0031-3203 | 1 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haigen Hu | 1 | 44 | 8.47 |
| Leizhao Shen | 2 | 1 | 0.34 |
| Qiu Guan | 3 | 43 | 9.92 |
| Xiaoxin Li | 4 | 1 | 0.68 |
| Qianwei Zhou | 5 | 1 | 0.34 |
| Ruan Su | 6 | 559 | 53.00 |