A Review of Deep Learning on Medical Image Analysis
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A Review of Deep Learning on Medical Image Analysis Jian Wang 1 & Hengde Zhu 1 & Shui-Hua Wang 1,2,3 & Yu-Dong Zhang 1,4 Accepted: 20 October 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Compared with common deep learning methods (e.g., convolutional neural networks), transfer learning is characterized by simplicity, efficiency and its low training cost, breaking the curse of small datasets. Medical image analysis plays an indispensable role in both scientific research and clinical diagnosis. Common medical image acquisition methods include Computer Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound (US), X-Ray, etc. Although these medical imaging methods can be applied for non-invasive qualitative and quantitative analysis of patients—compared with image datasets in other computer vision fields such like faces—medical images, especially its labeling, is still scarce and insufficient. Therefore, more and more researchers adopted transfer learning for medical image processing. In this study, after reviewing one hundred representative papers from IEEE, Elsevier, Google Scholar, Web of Science and various sources published from 2000 to 2020, a comprehensive review is presented, including (i) structure of CNN, (ii) background knowledge of transfer learning, (iii) different types of strategies performing transfer learning, (iv) application of transfer learning in various sub-fields of medical image analysis, and (v) discussion on the future prospect of transfer learning in the field of medical image analysis. Through this review paper, beginners could receive an overall and systematic knowledge of transfer learning application in medical image analysis. And policymaker of related realm will benefit from the summary of the trend of transfer learning in medical imaging field and may be encouraged to make policy positive to the future development of transfer learning in the field of medical image analysis. Keywords Transfer learning . Medical image analysis . CT . Deep learning . MRI . Convolutional neural networks . Fine-tuning . Feature extractor . Artificial intelligence
1 Introduction
* Shui-Hua Wang [email protected] * Yu-Dong Zhang [email protected] Jian Wang [email protected] Hengde Zhu [email protected] 1
School of Informatics, University of Leicester, Leicester LE1 7RH, UK
2
School of Architecture Building and Civil engineering, Loughborough University, Loughborough LE11 3TU, UK
3
School of Mathematics and Actuarial Science, University of Leicester, Leicester LE1 7RH, UK
4
Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Medicine is a science that benefits all mankind, directly related to everyone’s health and quality of life. As a result, medicine has always been one of the most highly regarded disciplines in the world. Medical research is inseparable from the support of medical image analysis. Both the cutting-edge medical research conducted in the laboratory and th
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