Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Models Genesis: Generic Autodidactic Models for 3D Medical Image
  Analysis   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
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 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 our 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 yet recurrent anatomy in medical
images can serve as strong supervision signals for deep models to learn common
anatomical representation automatically via self-supervision. As open science,
all pre-trained Models Genesis are available at
https://github.com/MrGiovanni/ModelsGenesis.

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The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom