CancerNet-SCa: Tailored Deep Neural Network Designs for Detection of
Skin Cancer from Dermoscopy Images
- URL: http://arxiv.org/abs/2011.10702v1
- Date: Sat, 21 Nov 2020 02:17:59 GMT
- Title: CancerNet-SCa: Tailored Deep Neural Network Designs for Detection of
Skin Cancer from Dermoscopy Images
- Authors: James Ren Hou Lee, Maya Pavlova, Mahmoud Famouri, and Alexander Wong
- Abstract summary: Skin cancer continues to be the most frequently diagnosed form of cancer in the U.S.
In this study we introduce CancerNet-SCa, a suite of deep neural network designs tailored for the detection of skin cancer from dermoscopy images.
- Score: 71.68436132514542
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Skin cancer continues to be the most frequently diagnosed form of cancer in
the U.S., with not only significant effects on health and well-being but also
significant economic costs associated with treatment. A crucial step to the
treatment and management of skin cancer is effective skin cancer detection due
to strong prognosis when treated at an early stage, with one of the key
screening approaches being dermoscopy examination. Motivated by the advances of
deep learning and inspired by the open source initiatives in the research
community, in this study we introduce CancerNet-SCa, a suite of deep neural
network designs tailored for the detection of skin cancer from dermoscopy
images that is open source and available to the general public as part of the
Cancer-Net initiative. To the best of the authors' knowledge, CancerNet-SCa
comprises of the first machine-designed deep neural network architecture
designs tailored specifically for skin cancer detection, one of which
possessing a self-attention architecture design with attention condensers.
Furthermore, we investigate and audit the behaviour of CancerNet-SCa in a
responsible and transparent manner via explainability-driven model auditing.
While CancerNet-SCa is not a production-ready screening solution, the hope is
that the release of CancerNet-SCa in open source, open access form will
encourage researchers, clinicians, and citizen data scientists alike to
leverage and build upon them.
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