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The intrinsic details of workings of DL models are a mystery to many. This volume strives to solve this mystery. The most popular DL models for medical imaging are convolution neural networks (CNN),VGG-SegNet, UNet etc. A brief outline is provided over these models. A chief or main advantage of these DL models is that the same architecture can be used for different types of data i.e., image, signals etc.
Over the years various DL models have been proposed to solve different problems in clinical sciences and medical imaging such as gene expression and biomedical imaging. Some genetic diseases can be identified only if particular gene is present or not. This involves a laborious process of gene data extraction, sequencing, identification etc., considering the fact that there are millions kind of genes. This volume discusses in brief some of DL-based applications in gene identification. The most popular application of DL is in medical imaging. The source of these images can be different, i.e., CT, MRI and US.
Mainak Biswas, PhD, computer scientist with specialization in application of machine learning and deep learning in biomedical domain. His research is inspired from providing an effective solution for computer aided diagnosis for diverse diseases. His PhD specialization was in application of advanced machine learning and deep learning in complex tissue characterization and segmentation from ultrasound images of liver and carotid arteries.
His other interests are development of advanced machine learning architectures and early warning systems for risk estimation of both symptomatic and asymptomatic patients at high risk of CVDs. He has published and presented more than 35 papers in the area of characterization and segmentation of ultrasound images through machine and deep learning platforms. Dr. Mainak Biswas completed B.Tech from Government College of Engineering and Ceramic Technology under West Bengal University of Technology, Kolkata, M.Tech from Jadavpur University and PhD from National Institute of Technology Goa, INDIA. Currently, he is serving as Associate Professor at Kalinga Institute of Industrial Technology, Bhubaneswar, INDIA.
Jasjit S. Suri, an innovator, a visionary, a scientist, and an internationally known world leader, has spent over 30 years in the field of biomedical engineering/sciences, software and hardware engineering and its management. During his career in biomedical industry/imaging, he has had an upstream growth and responsibilities from scientific engineer, scientist, manager, Director R&D, Sr. Director, Vice President, Chief Technology Officer (CTO), CEO level positions in industries like Siemens Medical Systems, Philips Medical Systems, Fisher Imaging Corporation and Eigen Inc., Global Biomedical Technologies Inc., AtheroPoint(TM), respectively and managed up to a maximum of 100 people.
Dr. Suri is a pioneer in the area of Computer Vision and Artificial Intelligence (AI), and has published over 1000+ International Journals covering several fields including 150+ articles in Vascular, Coronary, Prostate, Mammography, Diabetes, and COVID-19 CT lung area. Dr. Suri has conducted over 50 national and international seminars around the globe. He received his Masters from University of Illinois, Chicago, Doctorate from University of Washington, Seattle, and Masters in Business Administration (MBA) from Weatherhead School of Management, Case Western Reserve University (CWRU), Cleveland. He currently lives in California, USA.
1: An Overview of AI Applications in Medical Imaging for COVID-19-Related Brain and Heart Injuries
2: Characterizing Acute Respiratory Distress Syndrome in COVID-19: A Narrative Review of Artificial Intelligence-based Lung Analysis
3: A multicenter study using COVLIAS 2.0: eight pruned deep learning models for efficient COVID-19 CT lung segmentation and lesion localization
4: An investigation of the inter-variability in COVLIAS 1.0: Hybrid deep learning approaches for segmenting COVID-19 lungs in CT scans
5: A Comparative Analysis of Tuberculosis-Infected Lung X-Ray Image Segmentation: UNet vs. UNet++
An overview of Deep Learning and its applications in cardiovascular diseases
6: Reviewing the Role of Artificial Intelligence in Heart and Vascular Ultrasound: Advancements and Applications
7: A Deep Learning Perspective in Internal Carotid Artery and Bulb Segmentation: Review
8: Deep Learning Framework for Ultrasound-based Carotid Artery Disease Management: A Review
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