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Hodgkin Disease. --- Hodgkin's disease --- Hodgkin disease --- Hodgkin lymphoma --- Hodgkin's lymphoma --- Lymphadenoma --- Lymphogranuloma --- Lymphogranulomatosis --- Lymphosarcoma --- Pseudoleucemia --- Lymphomas --- Granuloma, Hodgkin's --- Granuloma, Hodgkins --- Hodgkin Lymphoma, Adult --- Hodgkin's Disease --- Hodgkin's Lymphoma --- Hodgkins Disease --- Lymphocyte Depletion Hodgkin's Lymphoma --- Lymphocyte-Rich Classical Hodgkin's Lymphoma --- Mixed Cellularity Hodgkin's Lymphoma --- Nodular Lymphocyte-Predominant Hodgkin's Lymphoma --- Nodular Sclerosing Hodgkin's Lymphoma --- Granuloma, Hodgkin --- Granuloma, Malignant --- Hodgkin Lymphoma --- Lymphogranuloma, Malignant --- Adult Hodgkin Lymphoma --- Disease, Hodgkin --- Disease, Hodgkin's --- Disease, Hodgkins --- Hodgkin Granuloma --- Hodgkin's Granuloma --- Hodgkins Granuloma --- Hodgkins Lymphoma --- Lymphocyte Rich Classical Hodgkin's Lymphoma --- Lymphogranulomas, Malignant --- Lymphoma, Hodgkin --- Lymphoma, Hodgkin's --- Malignant Granuloma --- Malignant Granulomas --- Malignant Lymphogranuloma --- Malignant Lymphogranulomas --- Nodular Lymphocyte Predominant Hodgkin's Lymphoma --- Reed-Sternberg Cells --- Hodgkin's disease. --- Hodgkin Disease
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Obesity and related co-morbidities are increasing worldwide and pose a serious health problem. Changes in lifestyle and diet would be the best remedies to fight obesity; however, many people will still rely on medical aid. Marine organisms have been prolific in the production of bioactive compounds for many diseases, e.g., cancer, and promise to be an excellent source for natural-derived molecules and novel nutraceuticals. Bioactive compounds with beneficial activities towards obesity have been described from diverse marine organism including marine algae, bacteria, sponges, fungi, crustaceans or fish. This Special Issue will highlight the progress in the following topics: Bioactive compounds for the treatment of obesity and obesity-related co-morbidities (diabetes, fatty liver, hyperlipidemia) from marine organisms; the isolation of novel compounds, the bioactivity screening of marine organisms and the elucidation of molecular mode of action of marine bioactive compounds.
natural compounds --- anti-obesity drugs --- high fat diet --- Ishige okamurae --- fat --- zebrafish Nile red fat metabolism assay --- physical exercise --- JAK2-STAT3 --- metabolite profiling --- obesity --- chlorophyll derivatives --- brown seaweed --- Skate skin --- PPAR? --- marine alga --- marine biodiscovery --- skate skin --- lipolytic --- leptin --- uncoupling protein 1 --- 3T3-L1 cells --- glucolipid metabolism disorder --- nutrition --- bioactivity --- chitosan oligosaccharide --- diphlorethohydroxycarmalol (DPHC) --- nutraceuticals --- whole small animal models --- high-fat diet --- adipocyte --- dyslipidemia --- bioactivity screening --- peroxisome proliferator-activated receptor gamma --- white adipose tissue --- antiobesity --- fatty liver disease --- thermal proteome profiling --- inflammation --- cyanobacteria --- Raja kenojei --- Arthrospira maxima --- cellularity --- adipocytes --- bioactive compound --- collagen peptide --- double-blind --- bisabolane-related compounds --- proliferation --- fatty acid metabolism --- cholesterol metabolism --- collagen --- randomized controlled trial --- mechanisms of action --- murine pre-adipocytes --- adipogenesis --- fucan --- marine sponges --- label-free quantitative proteomics --- diabetes --- body fat
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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.
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 --- 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
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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.
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
Choose an application
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.
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
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