  Transfer learning from natural images to medical images 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 and existing pre-trained 3D
models 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.
