Name
Abstract | ||
|---|---|---|
Cervical cancer remains a significant cause of mortality in low-income countries. As in many other diseases, the existence of several screening/diagnosis methods and subjective physician preferences creates a complex ecosystem for automated methods. In order to diminish the amount of labeled data from each modality/expert we propose a regularization-based transfer learning strategy that encourages source and target models to share the same coefficient signs. We instantiated the proposed framework to predict cross-modality individual risk and cross-expert subjective quality assessment of colposcopic images for different modalities. Thus, we are able to transfer knowledge gained from one expert/modality to another. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-58838-4_27 | PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2017) |
Keywords | Field | DocType |
Transfer learning, Regularization, Cervical cancer, Digital colposcopy | Modalities,Cervical cancer,Observability,Pattern recognition,Control theory,Computer science,Transfer of learning,Regularization (mathematics),Artificial intelligence,Labeled data,Machine learning | Conference |
Volume | ISSN | Citations |
10255 | 0302-9743 | 14 |
PageRank | References | Authors |
0.63 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kelwin Fernandes | 1 | 36 | 7.71 |
| Jaime S. Cardoso | 2 | 543 | 68.74 |
| Jessica Fernandes | 3 | 17 | 1.08 |