Name
Abstract | ||
|---|---|---|
Due to the importance of nuclear structure in cancer diagnosis, several predictive models have been described for diagnosing a wide variety of cancers based on nuclear morphology. In many computer-aided diagnosis (CAD) systems, cancer detection tasks can be generally formulated as set classification problems, which can not be directly solved by classifying single instances. In this paper, we propo... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JBHI.2018.2803793 | IEEE Journal of Biomedical and Health Informatics |
Keywords | Field | DocType |
Cancer,Prototypes,Kernel,Training,Predictive models,Cancer detection,Support vector machines | CAD,Kernel (linear algebra),Pattern recognition,Computer science,Support vector machine,Cancer detection,Artificial intelligence,Classifier (linguistics),Discriminative model,Decision boundary,Cancer | Journal |
Volume | Issue | ISSN |
23 | 1 | 2168-2194 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liu, C. | 1 | 10 | 1.28 |
| Yue Huang | 2 | 317 | 29.82 |
| John A Ozolek | 3 | 150 | 11.13 |
| Matthew G Hanna | 4 | 0 | 0.34 |
| Rajendra Singh | 5 | 0 | 0.34 |
| Gustavo K. Rohde | 6 | 395 | 41.81 |