Models Genesis
release_mowondvnvngx7enjg3elzwh6bu
by
Zongwei Zhou, Vatsal Sodha, Jiaxuan Pang, Michael B. Gotway, Jianming Liang
2020
Abstract
Transfer learning from natural image to medical image has been established as
one of the most practical paradigms in deep learning for medical image
analysis. To fit this paradigm, however, 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, thereby inevitably compromising its
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 (learnt by self-supervision),
and generic (served as source models for generating application-specific target
models). Our extensive experiments demonstrate that our Models Genesis
significantly outperform learning from scratch in all five target 3D
applications covering both segmentation and classification. More importantly,
learning a model from scratch simply in 3D may not necessarily yield
performance better than transfer learning from ImageNet in 2D, but our Models
Genesis consistently top any 2D/2.5D approaches including fine-tuning the
models pre-trained from ImageNet as well as fine-tuning the 2D versions of our
Models Genesis, confirming the importance of 3D anatomical information and
significance of Models Genesis for 3D medical imaging. This performance is
attributed to our unified self-supervised learning framework, built on a simple
yet powerful observation: the sophisticated and recurrent anatomy in medical
images can serve as strong yet free supervision signals for deep models to
learn common anatomical representation automatically via self-supervision. As
open science, all codes and pre-trained Models Genesis are available at
https://github.com/MrGiovanni/ModelsGenesis
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