Abstract
Importance. With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active research area both in medical industry and academia. This paper reviewed the recent progress of deep learning research in medical image analysis and clinical applications. It also discussed the existing problems in the field and provided possible solutions and future directions.Highlights. This paper reviewed the advancement of convolutional neural network-based techniques in clinical applications. More specifically, state-of-the-art clinical applications include four major human body systems: the nervous system, the cardiovascular system, the digestive system, and the skeletal system. Overall, according to the best available evidence, deep learning models performed well in medical image analysis, but what cannot be ignored are the algorithms derived from small-scale medical datasets impeding the clinical applicability. Future direction could include federated learning, benchmark dataset collection, and utilizing domain subject knowledge as priors.Conclusion. Recent advanced deep learning technologies have achieved great success in medical image analysis with high accuracy, efficiency, stability, and scalability. Technological advancements that can alleviate the high demands on high-quality large-scale datasets could be one of the future developments in this area.
Highlights
With rapid developments of artificial intelligence (AI) technology, the use of AI technology to mine clinical data has become a major trend in medical industry [1]
We review the latest developments in the field of medical image analysis with comprehensive and representative clinical applications
The generator was trained to estimate rigid transformation where the discriminator was used to distinguish between images that were aligned by ground-truth transformations or by predicted ones
Summary
With rapid developments of artificial intelligence (AI) technology, the use of AI technology to mine clinical data has become a major trend in medical industry [1]. Recent applications of deep leaning in medical image analysis involve various computer vision-related tasks such as classification, detection, segmentation, and registration. Classification, detection, and segmentation are fundamental and most widely used tasks. The most comprehensive review paper is the work of Litjens et al published in 2017 [12]. Deep learning is such a quickly evolving research field; numerous state-of-the-art works have been proposed since . We review the latest developments in the field of medical image analysis with comprehensive and representative clinical applications. We briefly review the common medical imaging modalities as well as technologies for various specific tasks in medical image analysis including classification, detection, segmentation, and registration. We give more detailed clinical applications with respect to different types of diseases and discuss the existing problems in the field and provide possible solutions and future research directions
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