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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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Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP) / Jorge Crichigno, Norbert Herencsar, Francesco Benedetto
| Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP) / Jorge Crichigno, Norbert Herencsar, Francesco Benedetto |
| Autore | Crichigno Jorge |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (194 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
similarity measure
dynamic time warping Least Absolute Shrinkage and Selection Operator (LASSO) multispectral information transmission convergence layer 3D segmentation micrographia MATLAB neural network wireless communication identification interference alignment Parkinson's disease dysgraphia NG-PON2 timing GPON semantic segmentation fractional-order filters maximum likelihood criterion kinematic analysis multitemporal data fractional calculus multi-hop relay network u-net interference leakage Richardson iteration activation process acoustic analysis follow-up study fractional-order derivative electrocardiogram (ECG) deep learning security modulo M quasi-stationary cognitive radio low-pass filters time-interleaved analog-to-digital converter (TIADC) sample-and-hold (S/H) mismatch authentication pattern recognition online handwriting sparse inference Taylor series EPON open-source spine machine learning brain signal representation magnitude responses Chebyshev filters XG-PON phonation hypokinetic dysarthria Parkinson's disease overcomplete multi-scale dictionary construction |
| ISBN |
9783039210411
3039210416 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910674032703321 |
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Crichigno Jorge
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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