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Advanced Computational Methods for Oncological Image Analysis
| Advanced Computational Methods for Oncological Image Analysis |
| Autore | Rundo Leonardo |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (262 p.) |
| Soggetto topico | Medicine |
| Soggetto non controllato |
melanoma detection
deep learning transfer learning ensemble classification 3D-CNN immunotherapy radiomics self-attention breast imaging microwave imaging image reconstruction segmentation unsupervised machine learning k-means clustering Kolmogorov-Smirnov hypothesis test statistical inference performance metrics contrast source inversion brain tumor segmentation magnetic resonance imaging survey brain MRI image tumor region skull stripping region growing U-Net BRATS dataset incoherent imaging clutter rejection breast cancer detection MRgFUS proton resonance frequency shift temperature variations referenceless thermometry RBF neural networks interferometric optical fibers breast cancer risk assessment machine learning texture mammography medical imaging imaging biomarkers bone scintigraphy prostate cancer semisupervised classification false positives reduction computer-aided detection breast mass mass detection mass segmentation Mask R-CNN dataset partition brain tumor classification shallow machine learning breast cancer diagnosis Wisconsin Breast Cancer Dataset feature selection dimensionality reduction principal component analysis ensemble method |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557353503321 |
Rundo Leonardo
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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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Deep Learning-Based Action Recognition
| Deep Learning-Based Action Recognition |
| Autore | Lee Hyo Jong |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (240 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
human action recognition
graph convolution high-order feature spatio-temporal feature feature fusion dynamic gesture recognition multi-modalities network class regularization 3D-CNN spatiotemporal activations class-specific features Dynamic Hand Gesture Recognition human-computer interaction hand shape features pose estimation stacked hourglass network deep learning convolutional receptive field hand gesture recognition human-machine interface artificial intelligence feedforward neural networks spatio-temporal image formation human activity recognition fusion strategies transfer learning activity recognition data augmentation multi-person pose estimation partitioned centerpose network partition pose representation continuous hand gesture recognition gesture spotting gesture classification multi-modal features 3D skeletal CNN spatiotemporal feature embedded system real-time action recognition Long Short-Term Memory spatio-temporal differential |
| ISBN | 3-0365-5200-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910619465803321 |
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Lee Hyo Jong
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| MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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