Deep learning has attracted great attention in the medical imaging community as a
promising solution for automated, fast and accurate medical image analysis, which is mandatory
for quality healthcare. Convolutional neural networks and its variants have become the most preferred
and widely used deep learning models in medical image analysis. In this paper, concise
overviews of the modern deep learning models applied in medical image analysis are provided
and the key tasks performed by deep learning models, i.e. classification, segmentation, retrieval,
detection, and registration are reviewed in detail. Some recent researches have shown that deep
learning models can outperform medical experts in certain tasks. With the significant breakthroughs
made by deep learning methods, it is expected that patients will soon be able to safely
and conveniently interact with AI-based medical systems and such intelligent systems will actually
improve patient healthcare. There are various complexities and challenges involved in deep
learning-based medical image analysis, such as limited datasets. But researchers are actively
working in this area to mitigate these challenges and further improve health care with AI.
Keywords: Classification, deep learning, detection, medical image analysis, segmentation, retrieval, registration.
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