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Artificial Intelligence in Medical Image Processing and Segmentation
| Artificial Intelligence in Medical Image Processing and Segmentation |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2023 |
| Descrizione fisica | 1 online resource (348 p.) |
| Soggetto topico |
History of engineering & technology
Technology: general issues |
| Soggetto non controllato |
2D ultrasound image
2D/3D registration 3D virtual reconstruction ALO artificial hummingbird algorithm artificial intelligence artificial neural network (ANN) attention mechanism auto-segmentation automatic volume measurement breast cancer breast density CAD canonical correlation analysis (CCA) carbon ion radiotherapy CBCT cervical cancer cervical net children CNN comparison computer vision applications convolutional neural network Convolutional Neural Networks Cranio-Maxillofacial surgery CT DarkNet-19 deep learning deep learning structures DICOM directional total variation disease discrimination accuracy dual-energy CT edge computing ensemble learning feature fusion feature selection fundus image GAN Grad-CAM hematoxylin eosin histopathological histopathology histopathology images image enhancement image inpainting image normalization image preprocessing image registration image segmentation image-guided radiotherapy in-house instance segmentation k-nearest neighbour (KNN) lesion segmentation limited-angular range loss function magnetic resonance imaging mandible mask-transformer-based networks medical image analysis medical imaging mitotic nuclei classification MobileNet mp-MRI MRI MRI guidance MRI-only multi-contrast MRI NasNet neuroimaging nuclei detection nuclei segmentation OCT orthogonal X-ray osteoarthritis ovarian tumor PA panoptic segmentation panoramic radiographs pap smear particle therapy patch size PCA PCNSL performance comparisons prostate cancer prostate segmentation pyramidal network radiomics random forest (RF) rare neurodevelopmental disorder rare tumor ResNet-101 safranin O fast green scale-adaptive segmentation semantic segmentation shuffle net ShuffleNet software support vector machine support vector machine (SVM) synthetic CT teeth segmentation textural tooth disease recognition tuberous sclerosis complex two-step method U-Net ultrasound bladder scanner urinary disease |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910743276403321 |
| MDPI - Multidisciplinary Digital Publishing Institute, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Systems Radiology and Personalized Medicine
| Systems Radiology and Personalized Medicine |
| Autore | de Jong Pim A |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (180 p.) |
| Soggetto topico | Medicine and Nursing |
| Soggetto non controllato |
[11C]mHED
[123I]mIBG [124I]mIBG [18F]F-DOPA [18F]FDG [18F]mFBG [68Ga]Ga-DOTA peptides adiposity artificial intelligence atherosclerosis bloodstream infection body composition calcification pattern cardiorenal syndrome cerebral aneurysm chest X-ray chronic limb-threatening ischemia computational fluid dynamics computed tomography contrast media convolutional neural network COVID-19 cyst infection deep learning diffuse idiopathic skeletal hyperostosis endocarditis FDG-PET FDG-PET/CT Grad-CAM hemodynamic image analysis imaging imaging biomarker infection intra-abdominal fat large vessel vasculitis morphological MRI n/a neuroblastoma non-contrast nuclear medicine osteoarthritis peripheral arterial disease QFlow radiological imaging radionuclide imaging radiotracers reliability risk factors rupture spondylodiscitis tissue characterization total body PET/CT TRANCE vascular graft infection venography white blood cell scintigraphy |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557748903321 |
de Jong Pim A
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||