Reprint
Deep Learning in Medical Image Analysis
Edited by
August 2021
458 pages
- ISBN978-3-0365-1469-7 (Hardback)
- ISBN978-3-0365-1470-3 (PDF)
This book is a reprint of the Special Issue Deep Learning in Medical Image Analysis that was published in
Computer Science & Mathematics
Engineering
Physical Sciences
Summary
The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis.
Format
- Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
interpretable/explainable machine learning; image classification; image processing; machine learning models; white box; black box; cancer prediction; deep learning; multimodal learning; convolutional neural networks; autism; fMRI; texture analysis; melanoma; glcm matrix; machine learning; classifiers; explainability; explainable AI; XAI; deep learning; medical imaging; diagnosis; ARMD; change detection; unsupervised learning; medical imaging; microwave breast imaging; image reconstruction; tumor detection; convolutional neural networks; deep learning; digital pathology; whole slide image processing; multiple instance learning; convolutional neural networks; deep learning classification; HER2; image classification; convolutional neural networks; deep learning; medical images; transfer learning; optimizers; neo-adjuvant treatment; digital pathology; tumour cellularity; machine learning; cancer; breast cancer; diagnostics; imaging; computation; artificial intelligence; 3D segmentation; machine learning; active surface; discriminant analysis; PET imaging; deep learning; medical image analysis; breast cancer; brain tumor; cervical cancer; colon cancer; lung cancer; transfer learning; computer vision; convolutional neural networks; image classification; musculoskeletal images; deep learning; medical images; deep learning; lung disease detection; taxonomy; medical images; convolutional neural network; CycleGAN; data augmentation; dermoscopic images; domain transfer; macroscopic images; skin lesion segmentation; infection detection; COVID-19; X-ray images; image classification; bayesian inference; shifted-scaled dirichlet distribution; MCMC; gibbs sampling; object detection; surgical tools; open surgery; egocentric camera; computers in medicine; segmentation; machine learning; deep learning; MRI; ECG signal detection; portable monitoring devices; 1D-convolutional neural network; deep learning; medical image segmentation; domain adaptation; meta-learning; U-Net; deep learning; medical image segmentation; computed tomography (CT); magnetic resonance imaging (MRI); computed tomography (CT); image reconstruction; low-dose; sparse-angle; deep learning; quantitative comparison; n/a