Name
Abstract | ||
|---|---|---|
Transfer learning is a standard technique to transfer knowledge from one domain to another. For applications in medical imaging, transfer from ImageNet has become the de-facto approach, despite differences in the tasks and im-age characteristics between the domains. However, it is un-clear what factors determine whether - and to what extent- transfer learning to the medical domain is useful. The long- standing assumption that features from the source domain get reused has recently been called into question. Through a series of experiments on several medical image bench-mark datasets, we explore the relationship between transfer learning, data size, the capacity and inductive bias of the model, as well as the distance between the source and tar-get domain. Our findings suggest that transfer learning is beneficial in most cases, and we characterize the important role feature reuse plays in its success. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPR52688.2022.00901 | IEEE Conference on Computer Vision and Pattern Recognition |
Keywords | DocType | Volume |
Transfer/low-shot/long-tail learning, Deep learning architectures and techniques, Medical,biological and cell microscopy | Conference | 2022 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christos Matsoukas | 1 | 0 | 0.34 |
| Johan Fredin Haslum | 2 | 0 | 0.34 |
| Moein Sorkhei | 3 | 0 | 0.34 |
| Magnus Söderberg | 4 | 0 | 0.34 |
| Kevin Smith | 5 | 2430 | 88.78 |