Bøger af Jasjit (The American Institute for Medical and Biological Engineering Suri

This volume discusses various diseases related to lung, heart, peripheral arterial imaging, and miscellaneous topics like gene expression characterization and classification. Further, the Vol. 2 discusses imaging applications, their complexities and the DL-models employed to resolve them in detail. The DL-based applications are categorized into two types: segmentation and characterization.The segmentation is chiefly involved in dissecting region-of-interest (ROI) of the infected part. In the characterization part, the dissected ROI or the overall image is graded as per the risk factor is involved. DL has a remarkable success in segmenting carotid plaque area from ultrasound common carotid artery images. Similarly in case of brain imaging, DL-based applications for brain cancer are divided into segmentation and characterization. In the segmentation part, the brain tumour is separated from the healthy tissue. In the characterization part, the tumour cells are graded as per their risk. Overall, DL is human brain comparable intelligence system which can strengthen effective medical treatment in a faster way. It is for sure, that DL-based technologies can enable doctors to quickly diagnose the patients, provide an effective plan for treatment and help in saving lives.Key features: Discusses various diseases related to lung, heart, peripheral arterial imaging, and miscellaneous topics like gene expression characterization and classificationDiscusses imaging applications, their complexities and the DL-models employed to resolve them in detailTakes the most basic workable model and then builds the entire architecture in a bottom-up approachProvides state-of-the-art contributions while addressing doubts in multimodal researchDetails the future of deep leanring and big data in medical imaging

This reference text explores breast cancer, Microwave scattering and microwave imaging based cancer detection. It also covers the basics of Microwave imaging and advanced methods in image reconstruction techniques. The role of machine learning and artificial intelligence in breast cancer diagnosis is also discussed.

This research and reference text explores the finer details of Deep Learning models. It provides a brief outline on popular models including convolution neural networks (CNN), deep belief networks (DBN), autoencoders, residual neural networks (Res Nets). The text discusses some of the Deep Learning-based applications in gene identification. Sections in the book explore the foundation and necessity of deep learning in radiology, the application of deep learning in the area of cardiovascular imaging and deep learning applications in the area of fatty liver disease characterization and COVID19, respectively.This reference text is highly relevant for medical professionals and researchers in the area of AI in medical imaging. Key Features: Discusses various diseases related to lung, heart, peripheral arterial imaging, as well as gene expression characterization and classificationExplores imaging applications, their complexities and the Deep Learning models employed to resolve them in detailProvides state-of-the-art contributions while addressing doubts in multimodal researchDetails the future of deep learning and big data in medical imaging