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Information Technology in Biomedicine / edited by Ewa Pietka, Pawel Badura, Jacek Kawa, Wojciech Wieclawek.
Format
Book
Language
English
Εdition
1st ed. 2021.
Published/Created
Cham : Springer International Publishing : Imprint: Springer, 2021.
Description
1 online resource (370 pages) : illustrations
Availability
Available Online
Springer Nature - Springer Intelligent Technologies and Robotics eBooks 2021 English International
Details
Subject(s)
Computational intelligence
[Browse]
Biomedical engineering
[Browse]
Health informatics
[Browse]
Editor
Pietka, Ewa
[Browse]
Pietka, Ewa
[Browse]
Badura, Pawel
[Browse]
Badura, Pawel
[Browse]
Kawa, Jacek
[Browse]
Kawa, Jacek
[Browse]
Wieclawek, Wojciech
[Browse]
Wieclawek, Wojciech
[Browse]
Pietka, Ewa
[Browse]
Badura, Pawel
[Browse]
Kawa, Jacek
[Browse]
Wieclawek, Wojciech
[Browse]
Series
Advances in Intelligent Systems and Computing, 1186
[More in this series]
Advances in Intelligent Systems and Computing, 2194-5357 ; 1186
[More in this series]
Summary note
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. .
Notes
Includes index.
Contents
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 Influence 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.
Show 5 more Contents items
ISBN
3-030-49666-X
Doi
10.1007/978-3-030-49666-1
Statement on language in description
Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage.
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