Name
Abstract | ||
|---|---|---|
•Producing dense feature maps and eliminating the need for a decoder phase to reconstruct missing features.•Designing a network that is as fast as FCN and outperforms state-of-the-art methods.•Segmenting skin lesions for melanoma detection accurately. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.compmedimag.2019.101658 | Computerized Medical Imaging and Graphics |
Keywords | Field | DocType |
Skin cancer,Melanoma,Deep neural networks,Dense pooling layer | Computer vision,Skin lesion,Lesion,Segmentation,Pooling,Skin cancer,Artificial intelligence,Medicine,Lesion segmentation | Journal |
Volume | ISSN | Citations |
78 | 0895-6111 | 2 |
PageRank | References | Authors |
0.39 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| E. Nasr-esfahani | 1 | 23 | 4.23 |
| Shima Rafiei | 2 | 3 | 2.78 |
| Mohammad H Jafari | 3 | 2 | 0.39 |
| Nader Karimi | 4 | 145 | 32.75 |
| James S Wrobel | 5 | 2 | 0.39 |
| Shadrokh Samavi | 6 | 233 | 38.99 |
| S. M. R. Soroushmehr | 7 | 71 | 21.08 |