Reprint
Artificial Intelligence Applied to Medical Imaging and Computational Biology
Edited by
February 2023
224 pages
- ISBN978-3-0365-6487-6 (Hardback)
- ISBN978-3-0365-6488-3 (PDF)
This book is a reprint of the Special Issue Artificial Intelligence Applied to Medical Imaging and Computational Biology that was published in
Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
Medical imaging and computational biology continuously pose new fundamental medical and biological questions that often give rise to novel challenges in Artificial Intelligence. These research fields present an increasing need for the application of cutting-edge computational approaches that generally involve machine learning or computational intelligence techniques, which can effectively perform bioimage and biosignal processing in different clinical areas.
Format
- Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
distribution patterns; fibroblast cells; HCT-8 colon-cancer cells; nature-inspired techniques; quantification; segmentation; myocardial infarction; vectorcardiogram; multivariate VMD; deep CNN; accuracy; deep learning; image segmentation; brain tumors; radiosurgery; magnetic resonance imaging; age-related macular degeneration; choroidal neovascularization; convolutional neural networks; image classification; optical coherence tomography angiography; brain metastasis; glioblastoma; machine learning; oxygenation; tumor infiltration; breast cancer detection; microwave breast imaging; computer-aided diagnosis (CAD); first-in-human (FiH) study; deep learning; semantic segmentation; triceps surae muscle; medical image segmentation; breast cancer; pattern recognition; machine learning; clinical feasibility; magnetic resonance imaging; computer-assisted segmentation; artificial intelligence; orthopedics; neural network; deep learning; segmentation; mandible; craniomaxillofacial bone; deep learning; neural network; multi-center; histopathology; lung cancer; supervised segmentation; unsupervised segmentation; stroke; fine tactile sensation; electroencephalography; machine learning; evaluation; n/a