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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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Artificial Intelligence in Oral Health
| Artificial Intelligence in Oral Health |
| Autore | Lee Jae-Hong |
| Pubbl/distr/stampa | Basel, : MDPI Books, 2022 |
| Descrizione fisica | 1 electronic resource (190 p.) |
| Soggetto topico | Medicine |
| Soggetto non controllato |
machine learning
artificial intelligence malocclusion diagnostic imaging active learning maxillary sinusitis convolutional neural network deep learning segmentation oral microbiota LEfSe PCoA alloprevotella prevotella core microbiota artificial neural networks oral cancer diagnosis oral cancer prediction pit and fissure sealants caries assessment visual examination clinical evaluation convolutional neural networks transfer learning deep learning network YOLOv4 mandibular third molar inferior alveolar nerve contact relationship panoramic radiograph deep learning methods caries diagnosis dental panoramic images radiography Fourier transform infrared spectroscopy FTIR imaging spectral biomarker multivariate analysis discriminant model oral squamous cell carcinoma oral epithelial dysplasia oral potentially malignant disorder risk stratification early oral cancer detection dentigerous cysts histopathology images image classification odontogenic keratocysts radicular cysts AI screening diagnosis dentistry ultrasonography tongue algorithm dysphagia impacted tooth detection neural networks proximal caries training strategy small dataset periapical radiograph X-ray tooth extraction oroantral fistula operative planning |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910595066803321 |
Lee Jae-Hong
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| Basel, : MDPI Books, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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