Name
Title | ||
|---|---|---|
Learning the retinal anatomy from scarce annotated data using self-supervised multimodal reconstruction |
Abstract | ||
|---|---|---|
Deep learning is becoming the reference paradigm for approaching many computer vision problems. Nevertheless, the training of deep neural networks typically requires a significantly large amount of annotated data, which is not always available. A proven approach to alleviate the scarcity of annotated data is transfer learning. However, in practice, the use of this technique typically relies on the availability of additional annotations, either from the same or natural domain. We propose a novel alternative that allows to apply transfer learning from unlabelled data of the same domain, which consists in the use of a multimodal reconstruction task. A neural network trained to generate one image modality from another must learn relevant patterns from the images to successfully solve the task. These learned patterns can then be used to solve additional tasks in the same domain, reducing the necessity of a large amount of annotated data. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.asoc.2020.106210 | Applied Soft Computing |
Keywords | DocType | Volume |
Deep learning,Eye fundus,Self-supervised learning,Optic disc,Blood vessels,Fovea,Medical imaging,Transfer learning | Journal | 91 |
ISSN | Citations | PageRank |
1568-4946 | 1 | 0.37 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Álvaro S. Hervella | 1 | 9 | 2.93 |
| José Rouco | 2 | 1 | 0.37 |
| J Novo | 3 | 89 | 18.73 |
| M Ortega | 4 | 235 | 37.13 |