Name
Abstract | ||
|---|---|---|
Deep learning is the current bet for image classification. Its greed for huge amounts of annotated data limits its usage in medical imaging context. In this scenario transfer learning appears as a prominent solution. In this report we aim to clarify how transfer learning schemes may influence classification results. We are particularly focused in the automated melanoma screening problem, a case of medical imaging in which transfer learning is still not widely used. We explored transfer with and without fine-tuning, sequential transfers and usage of pre-trained models in general and specific datasets. Although some issues remain open, our findings may drive future researches. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computer Vision and Pattern Recognition | Medical imaging,Computer science,Transfer of learning,Artificial intelligence,Deep learning,Contextual image classification,Machine learning |
DocType | Volume | Citations |
Journal | abs/1609.01228 | 3 |
PageRank | References | Authors |
0.44 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Afonso Menegola | 1 | 30 | 2.54 |
| Michel Fornaciali | 2 | 39 | 3.94 |
| Ramon Pires | 3 | 34 | 3.26 |
| Sandra Eliza Fontes de Avila | 4 | 377 | 15.15 |
| Eduardo Valle | 5 | 373 | 22.17 |