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Deep Learning in Medical Image Analysis
| Deep Learning in Medical Image Analysis |
| Autore | Zhang Yudong |
| Pubbl/distr/stampa | 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 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557435103321 |
Zhang Yudong
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Innovative Learning Environments in STEM Higher Education [[electronic resource] ] : Opportunities, Challenges, and Looking Forward / / edited by Jungwoo Ryoo, Kurt Winkelmann
| Innovative Learning Environments in STEM Higher Education [[electronic resource] ] : Opportunities, Challenges, and Looking Forward / / edited by Jungwoo Ryoo, Kurt Winkelmann |
| Autore | Ryoo Jungwoo |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Springer Nature, 2021 |
| Descrizione fisica | 1 online resource (XV, 137 p. 8 illus., 7 illus. in color.) |
| Disciplina | 519.5 |
| Collana | SpringerBriefs in Statistics |
| Soggetto topico |
Statistics
Machine learning Learning Instruction Knowledge representation (Information theory) Statistics for Social Sciences, Humanities, Law Machine Learning Statistics and Computing/Statistics Programs Learning & Instruction Knowledge based Systems Educació STEM Educació superior |
| Soggetto genere / forma | Llibres electrònics |
| Soggetto non controllato |
Statistics for Social Sciences, Humanities, Law
Machine Learning Statistics and Computing/Statistics Programs Learning & Instruction Knowledge based Systems Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy Statistics and Computing Education Innovative Learning Environments ILEs Science, Technology, Engineering, and Math STEM virtual reality VR augmented reality mixed reality cross reality extended reality artificial intelligence AI adaptive learning personalized learning higher education multimodal learning mobile learning Open Access Social research & statistics Mathematical & statistical software Teaching skills & techniques Cognition & cognitive psychology Expert systems / knowledge-based systems |
| ISBN | 3-030-58948-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction -- 2. X-FILEs Vision for personalized and Adaptive Learning -- 3. X-FILEs Vision for Multi-modal Learning Formats -- 4. X-FILEs Vision for Extended/Cross Reality (XR) -- 5. X-FILEs Vision for Artificial Intelligence (AI) and Machine Learning (ML) -- 6. Cross-Cutting Concerns -- 7. Epilogue. |
| Record Nr. | UNISA-996466564503316 |
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Ryoo Jungwoo
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| Springer Nature, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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