Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis.

ABSTRACT: Transfer learning from natural image to medical image has established as one of the most practical paradigms in deep learning for medical image analysis. However, to fit this paradigm, 3D imaging tasks in the most prominent imaging modalities (e.g., CT and MRI) have to be reformulated and solved in 2D, losing rich 3D anatomical information and inevitably compromising the performance. To overcome this limitation, we have built a set of models, called Generic Autodidactic Models, nicknamed Models Genesis, because they are created ex nihilo (with no manual labeling), self-taught (learned by self-supervision), and generic (served as source models for generating application-specific target models). Our extensive experiments demonstrate that our Models Genesis signifi

SUBMITTER: Zhou Z 

PROVIDER: S-EPMC7405596 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature