Reprint
Computational Medical Image Analysis
Edited by
June 2024
246 pages
- ISBN978-3-7258-1394-0 (Hardback)
- ISBN978-3-7258-1393-3 (PDF)
This book is a reprint of the Special Issue Computational Medical Image Analysis that was published in
Computer Science & Mathematics
Summary
This Special Issue covers a variety of computational applications in medical image analysis. With a vast majority of the articles reporting on machine learning or deep learning-based methodologies, it highlights the transistion toward artificial intelligence over the past decade.
Format
- Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
Keywords
chaotic attractors; computer vision; disease diagnosis; encryption; computer-assisted diagnosis; convolutional neural networks; compressed sensing; holography cardiac cine MRI; l1-norm smooth approximations; hyperbolic tangent function; soft thresholding; breast cancer detection; magnification dependent; histopathology; BreakHis; IDC; Xception model; ResNet50 model; EfficientNetB0; 40×; medical image dataset analysis; diagnosis; machine learning; deep learning; classification; convolutional neural networks; deep learning; digital pathology; histology image analysis; infrared thermal segmentation; regional neuraxial analgesia; deep learning; random fourier features; class activation maps; machine learning; artificial intelligence; oral health; X-ray imaging; diagnosis; convolutional neural networks; deep learning; electrophysiological (EEG) signals; support vector machine; random forest; decision tree; AdaBoost; Naive Bayes; linear discriminant analysis (LDA); machine learning; attention deficit hyperactivity disorder (ADHD); deep learning; skin cancer; image augmentation; GAN; geometric augmentation; image classification; interpretable technique; wound monitoring; computer vision; hybrid learning; image segmentation; superpixel; regression; virtual DSC-MRI examination; perfusion descriptors; tracer concentration curves; brain model; pathology simulation; machine learning; deep learning; convolutional neural networks; deep features; COVID-19; classification; CT scan; n/a