How much off-the-shelf knowledge is transferable from natural images to pathology images?

ABSTRACT: Deep learning has achieved a great success in natural image classification. To overcome data-scarcity in computational pathology, recent studies exploit transfer learning to reuse knowledge gained from natural images in pathology image analysis, aiming to build effective pathology image diagnosis models. Since transferability of knowledge heavily depends on the similarity of the original and target tasks, significant differences in image content and statistics between pathology images and natural images raise the questions: how much knowledge is transferable? Is the transferred information equally contributed by pre-trained layers? If not, is there a sweet spot in transfer learning that balances transferred model's complexity and performance? To answer these questions, this paper proposes

SUBMITTER: Li X 

PROVIDER: S-EPMC7556818 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature