Using Deep Symmetry_Sensitive Netwrok for Detecting Diseases in Symmetric Organs by Arko Barman
Department of Electrical Engineering Seminar Notice
Title: USING DEEP SYMMETRY_SENSITIVE NETWORK FOR DETECTING DISEASES IN SYMMETRIC ORGANS.
Speaker: Dr Arko Barman
Postdoctoral Research Scholar
School of Biomedical Informatics
University of Texas Health Science Centre at Houston
UT Health,TEXAS ,USA
Time/Date : 1600 hrs,10 Jan 2020,Friday
Venue : MMCR, Hall No. C241, I Floor, Dept. of EE
Spatial symmetry is commonly used by clinicians in the diagnosis and prognosis of diseases involving multiple organs such as brain, prostate, breasts, and lungs. Anomalies in symmetry can be indicative of patient-specific disease-related features that are less sensitive to inter-patient variability. However, quantifying these symmetric anomalies is challenging as the symmetries in the human body are not exact mirrored copies. This study involves the design and development of a novel deep learning architecture, named Deep Symmetry-sensitive Network (DeepSymNet), capable of learning anomalies in symmetry from minimally processed 2D or 3D images. Besides a synthetic dataset, DeepSymNet was evaluated for detection of Large Vessel Occlusion (LVO) in the brain for ischemic stroke patients (using 3D CT angiography images) and detection of breast cancer (using 2D mammogram images). DeepSymNet is less sensitive to noise, rotation and translation compared to a “symmetry-naive” deep convolutional neural network (CNN) with a similar number of parameters. Additionally, the DeepSymNet architecture is efficient during training and achieves performance similar to or better than asymmetry-naive CNN with orders of magnitude fewer training examples. Finally, an interpretation of the decision-making process of the DeepSymNet model using activation maps is presented. In our experiments, DeepSymNet automatically learned to focus on the regions of the brain/breast that clinicians observe for detecting LVO/breast cancer without any prior knowledge apart from symmetry and outcome variable.
Arko Barman is a postdoctoral research fellow at The University of Texas Health Science Center at Houston, where his current research focuses on developing imaging-based computer aided diagnosis systems for stroke, Alzheimer’s and other diseases. He received his B.E. degree in Electrical Engineering from Jadavpur University in 2009 and his M.E. degree in Signal Processing from Indian Institute of Science in 2011. He received his Ph.D. in Computer Science at the University of Houston in 2018. Dr. Barman has worked at Broadcom Corporation and PARC (A Xerox Company), and has also served as an Assistant Professor at an engineering college in India. His research interests include Medical Image Computing, Deep Learning, Computer Vision, Machine Learning, Data Mining, and Heuristic Optimization Algorithms. He has also been involved in curriculum design and has taught a diverse range of courses at diﬀerent levels.