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Information Technology in Biomedicine 1st ed. 2021 [Minkštas viršelis]

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  • Formatas: Paperback / softback, 386 pages, aukštis x plotis: 235x155 mm, weight: 605 g, 119 Illustrations, color; 20 Illustrations, black and white; X, 386 p. 139 illus., 119 illus. in color., 1 Paperback / softback
  • Serija: Advances in Intelligent Systems and Computing 1186
  • Išleidimo metai: 03-Sep-2020
  • Leidėjas: Springer Nature Switzerland AG
  • ISBN-10: 3030496651
  • ISBN-13: 9783030496654
Kitos knygos pagal šią temą:
  • Formatas: Paperback / softback, 386 pages, aukštis x plotis: 235x155 mm, weight: 605 g, 119 Illustrations, color; 20 Illustrations, black and white; X, 386 p. 139 illus., 119 illus. in color., 1 Paperback / softback
  • Serija: Advances in Intelligent Systems and Computing 1186
  • Išleidimo metai: 03-Sep-2020
  • Leidėjas: Springer Nature Switzerland AG
  • ISBN-10: 3030496651
  • ISBN-13: 9783030496654
Kitos knygos pagal šią temą:
The rapid and continuous growth in the amount of available medical information and the variety of multimodal content has created demand for a fast and reliable technology capable of processing data and delivering results in a user-friendly manner, whenever and wherever the information is needed. Multimodal acquisition systems, AI-powered applications, and biocybernetic support for medical procedures, physiotherapy and prevention have opened up exciting new avenues in terms of optimizing the healthcare system for the benefit of patients. This book presents a comprehensive study on the latest advances in medical data science and gathers carefully selected articles written by respected experts on information technology. Pursuing an interdisciplinary approach and addressing both theoretical and applied aspects, it chiefly focuses on: 





Artificial Intelligence





Image Analysis





Sound and Motion in Physiotherapy and Physioprevention





Modeling and Simulation





Medical Data Analysis





Given its scope, the book offers a valuable reference tool for all scientists who deal with problems of designing and implementing information processing tools employed in systems that assist in patient diagnosis and treatment, as well as students who want to learn more about the latest innovations in quantitative medical data analysis, data mining, and artificial intelligence.







 
Deep Learning Approach to Subepidermal Low Echogenic Band Segmentation
in High Frequency Ultrasound.- A Review of Clustering Methods in
Microorganism Image Analysis.- MRFU-Net: A Multiple Receptive Field U-Net for
Environmental Microorganism Image Segmentation.- Deep Learning Approach to
Automated Segmentation of Tongue in Camera Images for Computer-Aided Speech
Diagnosis.- 3-D Tissue Image Reconstruction from Digitized Serial Histologic
Sections to Visualize Small Tumor Nests in Lung Adenocarcinomas.- The
Inuence of Age on Morphometric and Textural Vertebrae Features in Lateral
Cervical Spine Radiographs.- Evaluation of Shape from Shading Surface
Reconstruction Quality for Liver Phantom.- Pancreas and Duodenum Automated
Organ Segmentation.