Deep Learning in Medical Image Analysis
Zhang, Yudong
Deep Learning in Medical Image Analysis - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (458 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-1470-3 9783036514697 9783036514703
10.3390/books978-3-0365-1470-3 doi
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 medical imaging diagnosis ARMD change detection unsupervised learning microwave breast imaging image reconstruction tumor detection digital pathology whole slide image processing multiple instance learning deep learning classification HER2 medical images transfer learning optimizers neo-adjuvant treatment tumour cellularity cancer breast cancer diagnostics imaging computation artificial intelligence 3D segmentation active surface discriminant analysis PET imaging medical image analysis brain tumor cervical cancer colon cancer lung cancer computer vision musculoskeletal images lung disease detection taxonomy convolutional neural network CycleGAN data augmentation dermoscopic images domain transfer macroscopic images skin lesion segmentation infection detection COVID-19 X-ray images bayesian inference shifted-scaled dirichlet distribution MCMC gibbs sampling object detection surgical tools open surgery egocentric camera computers in medicine segmentation MRI ECG signal detection portable monitoring devices 1D-convolutional neural network medical image segmentation domain adaptation meta-learning U-Net computed tomography (CT) magnetic resonance imaging (MRI) low-dose sparse-angle quantitative comparison n/a
Deep Learning in Medical Image Analysis - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (458 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-1470-3 9783036514697 9783036514703
10.3390/books978-3-0365-1470-3 doi
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 medical imaging diagnosis ARMD change detection unsupervised learning microwave breast imaging image reconstruction tumor detection digital pathology whole slide image processing multiple instance learning deep learning classification HER2 medical images transfer learning optimizers neo-adjuvant treatment tumour cellularity cancer breast cancer diagnostics imaging computation artificial intelligence 3D segmentation active surface discriminant analysis PET imaging medical image analysis brain tumor cervical cancer colon cancer lung cancer computer vision musculoskeletal images lung disease detection taxonomy convolutional neural network CycleGAN data augmentation dermoscopic images domain transfer macroscopic images skin lesion segmentation infection detection COVID-19 X-ray images bayesian inference shifted-scaled dirichlet distribution MCMC gibbs sampling object detection surgical tools open surgery egocentric camera computers in medicine segmentation MRI ECG signal detection portable monitoring devices 1D-convolutional neural network medical image segmentation domain adaptation meta-learning U-Net computed tomography (CT) magnetic resonance imaging (MRI) low-dose sparse-angle quantitative comparison n/a