Deep Learning: A Breakthrough in Medical Imaging
Abstract
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. U-Net is another popular architecture especially for biomedical imaging. It consists of a contraction and expansion path to pixel wise predict the dataset. This model is better than previously available medical image segmentation approaches. However again, it fails to produce the promising results with 3D voxels. For that, an incremental version of U-Net, Multiplanar U-Net has been developed in 2019. In this talk, 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, and detection are shown. Some recent research has 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 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.
Short Biography
Dr. Sandeep Singh Sengar is a Postdoctoral Research Fellow at the Machine Learning Section of Computer Science Department, University of Copenhagen, Denmark. He holds an M. Tech. degree in Information Security from Motilal Nehru National Institute of Technology, Allahabad, India, and a Ph.D. degree in Computer Science and Engineering from Indian Institute of Technology (ISM), Dhanbad, India. Dr. Sengar’s current research interests include medical image segmentation, motion segmentation, visual object tracking, object recognition, and video compression. His broader research interests include machine learning, computer vision, image/video processing and its applications. He has published several research articles in reputed international journals and conferences in the field of computer vision and image processing. He has also filed patent and submitted research projects in different research organizations. He is a Reviewer of several reputed International Transactions, Journals, and conferences including IEEE Transactions on Systems, Man and Cybernetics: Systems, Pattern Recognition, Neurocomputing, Optik. He has also served as a Technical Program Committee member in many International Conferences. He is serving as a Review Editor at Frontiers in Artificial Intelligence journal. He has organized several special sessions and given keynote presentations at International Conferences. In addition to these, he has also given many expert talks in reputed organizations.