Deep Learning in Medical Image Analysis

• Autore: Zhang Yudong
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (458 p.)
• Soggetto non controllato: 1D-convolutional neural network; 3D segmentation; active surface; ARMD; artificial intelligence; autism; bayesian inference; black box; brain tumor; breast cancer; cancer; cancer prediction; cervical cancer; change detection; classifiers; colon cancer; computation; computed tomography (CT); computer vision; computers in medicine; convolutional neural network; convolutional neural networks; COVID-19; CycleGAN; data augmentation; deep learning; deep learning classification; dermoscopic images; diagnosis; diagnostics; digital pathology; discriminant analysis; domain adaptation; domain transfer; ECG signal detection; egocentric camera; explainability; explainable AI; fMRI; gibbs sampling; glcm matrix; HER2; image classification; image processing; image reconstruction; imaging; infection detection; interpretable/explainable machine learning; low-dose; lung cancer; lung disease detection; machine learning; machine learning models; macroscopic images; magnetic resonance imaging (MRI); MCMC; medical image analysis; medical image segmentation; medical images; medical imaging; melanoma; meta-learning; microwave breast imaging; MRI; multimodal learning; multiple instance learning; musculoskeletal images; n/a; neo-adjuvant treatment; object detection; open surgery; optimizers; PET imaging; portable monitoring devices; quantitative comparison; segmentation; shifted-scaled dirichlet distribution; skin lesion segmentation; sparse-angle; surgical tools; taxonomy; texture analysis; transfer learning; tumor detection; tumour cellularity; U-Net; unsupervised learning; white box; whole slide image processing; X-ray images; XAI
• Persona (resp. second.): GorrizJuan Manuel; DongZhengchao; ZhangYudong
• Sommario/riassunto: 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.
• Titolo autorizzato: Deep Learning in Medical Image Analysis
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557435103321
• Lo trovi qui: Univ. Federico II