Name
Abstract | ||
|---|---|---|
In this paper, a multi-instance learning framework is introduced to solve the problem of skin biopsy image features recognition. Previously reported methods for skin surface images were mostly based on color features extraction. They are incapable to be directly applied to skin biopsy image features recognition because biopsy images are often dyed and have obvious inner structures with different textures. Therefore, we regard skin biopsy images as multi-instance samples, whose instances are regions or structures captured by applying Normalized Cut. Texture feature extraction methods are used to express each region as a vectorial expression. Then two multi-instance learning algorithms reported successful in various image retrieval tasks were applied. Nine features were manually selected as target features to evaluate the proposed method on a skin disease diagnosis datasets of 6579 biopsy images from 2010 to 2011. The result showed that the proposed method is effective and medically acceptable. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/BIBM.2012.6392648 | BIBM |
Keywords | DocType | Citations |
various image retrieval task,multi-instance sample,skin biopsy image,Normalized Cut,biopsy image,different texture,skin disease diagnosis datasets,skin surface image,texture feature extraction method,proposed method | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuyi Chen | 1 | 0 | 0.34 |
| Yunting Liang | 2 | 0 | 0.34 |
| Zhaohui Liang | 3 | 26 | 15.31 |
| Jian Yin | 4 | 105 | 6.71 |
| Xiangyang Shu | 5 | 0 | 0.68 |
| Gang Zhang | 6 | 0 | 0.34 |