Name
Abstract | ||
|---|---|---|
Lesion segmentation in skin images is an important step in computerized detection of skin cancer. Melanoma is known as one of the most life threatening types of this cancer. Existing methods often fall short of accurately segmenting lesions with fuzzy boarders. In this paper, a new class of fully convolutional network is proposed, with new dense pooling layers for segmentation of lesion regions in non-dermoscopic images. Unlike other existing convolutional networks, this proposed network is designed to produce dense feature maps. This network leads to highly accurate segmentation of lesions. The produced dice score here is 91.6% which outperforms state-of-the-art algorithms in segmentation of skin lesions based on the Dermquest dataset. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Skin lesion,Pattern recognition,Lesion,Segmentation,Computer science,Pooling,Skin cancer,Artificial intelligence,Lesion segmentation |
DocType | Volume | Citations |
Journal | abs/1712.10207 | 0 |
PageRank | References | Authors |
0.34 | 18 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| E. Nasr-esfahani | 1 | 23 | 4.23 |
| Shima Rafiee | 2 | 0 | 0.34 |
| Mohammad H. Jafari | 3 | 5 | 0.80 |
| Nader Karimi | 4 | 145 | 32.75 |
| James S. Wrobel | 5 | 0 | 0.34 |
| S. M. Reza Soroushmehr | 6 | 30 | 2.70 |
| Shadrokh Samavi | 7 | 233 | 38.99 |
| Kayvan Najarian | 8 | 262 | 59.53 |