Medical Deep Learning – A systematic Meta-Review
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by
Jan Egger, Christina Gsxaner, Antonio Pepe, Jianning Li
2020
Abstract
Deep learning had a remarkable impact in different scientific disciplines
during the last years. This was demonstrated in numerous tasks, where deep
learning algorithms were able to outperform the state-of-art methods, also in
image processing and analysis. Moreover, deep learning delivers good results in
tasks like autonomous driving, which could not have been performed
automatically before. There are even applications where deep learning
outperformed humans, like object recognition or games. Another field in which
this development is showing a huge potential is the medical domain. With the
collection of large quantities of patient records and data, and a trend towards
personalized treatments, there is a great need for an automatic and reliable
processing and analysis of this information. Patient data is not only collected
in clinical centres, like hospitals, but it relates also to data coming from
general practitioners, healthcare smartphone apps or online websites, just to
name a few. This trend resulted in new, massive research efforts during the
last years. In Q2/2020, the search engine PubMed returns already over 11.000
results for the search term 'deep learning', and around 90
publications are from the last three years. Hence, a complete overview of the
field of 'medical deep learning' is almost impossible to obtain and getting
a full overview of medical sub-fields gets increasingly more difficult.
Nevertheless, several review and survey articles about medical deep learning
have been presented within the last years. They focused, in general, on
specific medical scenarios, like the analysis of medical images containing
specific pathologies. With these surveys as foundation, the aim of this
contribution is to provide a very first high-level, systematic meta-review of
medical deep learning surveys.
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