Medical Deep Learning – A systematic Meta-Review
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by
Jan Egger, Christina Gsaxner, 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 cutting-edge methods, like in
image processing and analysis. Moreover, deep learning delivered
state-of-the-art results in tasks like autonomous driving, outclassing previous
attempts. There are even contexts where deep learning outperformed humans, like
object recognition and gaming. 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 automated and reliable processing and
analysis of health information. Patient data is not only collected in clinical
centres, like hospitals, but it relates also to data collected by general
practitioners, mobile healthcare 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 returned already over 11.000 results for the
search term 'deep learning', and around 90
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 becomes increasingly more difficult. Nevertheless, several
review and survey articles about medical deep learning have been presented
within the last years. They focus, 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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