Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective
These are exciting times for medical image processing. Innovations in deep learning and the increasing availability of large annotated medical image datasets are leading to dramatic advances in automated understanding of medical images. From this perspect
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Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective Ronald M. Summers
Abstract These are exciting times for medical image processing. Innovations in deep learning and the increasing availability of large annotated medical image datasets are leading to dramatic advances in automated understanding of medical images. From this perspective, I give a personal view of how computer-aided diagnosis of medical images has evolved and how the latest advances are leading to dramatic improvements today. I discuss the impact of deep learning on automated disease detection and organ and lesion segmentation, with particular attention to applications in diagnostic radiology. I provide some examples of how time-intensive and expensive manual annotation of huge medical image datasets by experts can be sidestepped by using weakly supervised learning from routine clinically generated medical reports. Finally, I identify the remaining knowledge gaps that must be overcome to achieve clinician-level performance of automated medical image processing systems. Computer-aided diagnosis (CAD) in medical imaging has flourished over the past several decades. New advances in computer software and hardware and improved quality of images from scanners have enabled this progress. The main motivations for CAD have been to reduce error and to enable more efficient measurement and interpretation of images. From this perspective, I will describe how deep learning has led to radical changes in how CAD research is conducted and in how well it performs. For brevity, I will include automated disease detection and image processing under the rubric of CAD.
Financial Disclosure The author receives patent royalties from iCAD Medical. Disclaimer No NIH endorsement of any product or company mentioned in this manuscript should be inferred. The opinions expressed herein are the author’s and do not necessarily represent those of NIH. R.M. Summers (B) Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bldg. 10, Room 1C224D MSC 1182, Bethesda, MD 20892-1182, USA e-mail: [email protected] URL: http://www.cc.nih.gov/about/SeniorStaff/ronald_summers.html © Springer International Publishing Switzerland 2017 L. Lu et al. (eds.), Deep Learning and Convolutional Neural Networks for Medical Image Computing, Advances in Computer Vision and Pattern Recognition, DOI 10.1007/978-3-319-42999-1_1
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R.M. Summers
For medical imaging, CAD has focused predominantly on radiology, cardiology, and pathology. Examples in radiology include the automated detection of microcalcifications and masses on mammography, lung nodules on chest X-rays and CT scans, and colonic polyps on CT colonography [1]. In cardiology, examples include CAD for echocardiography and angiography [2–4]. In digital pathology, examples include detection of cellular components such as nuclei and cells and diseases such as breast, cervical, and prostate cancers [5]. Despite significant rese
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