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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 online resource (262 p.) |
| Soggetto topico | Medicine and Nursing |
| Soggetto non controllato |
3D-CNN
bone scintigraphy brain MRI image brain tumor brain tumor segmentation BRATS dataset breast cancer breast cancer detection breast cancer diagnosis breast imaging breast mass classification clutter rejection computer-aided detection contrast source inversion dataset partition deep learning dimensionality reduction ensemble classification ensemble method false positives reduction feature selection image reconstruction imaging biomarkers immunotherapy incoherent imaging interferometric optical fibers k-means clustering Kolmogorov-Smirnov hypothesis test machine learning magnetic resonance imaging mammography Mask R-CNN mass detection mass segmentation medical imaging melanoma detection microwave imaging MRgFUS n/a performance metrics principal component analysis prostate cancer proton resonance frequency shift radiomics RBF neural networks referenceless thermometry region growing risk assessment segmentation self-attention semisupervised classification shallow machine learning skull stripping statistical inference survey temperature variations texture transfer learning tumor region U-Net unsupervised machine learning Wisconsin Breast Cancer Dataset |
| 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 online resource (240 p.) |
| Soggetto topico |
History of engineering & technology
Technology: general issues |
| Soggetto non controllato |
3D skeletal
3D-CNN action recognition activity recognition artificial intelligence class regularization class-specific features CNN continuous hand gesture recognition convolutional receptive field data augmentation deep learning dynamic gesture recognition Dynamic Hand Gesture Recognition embedded system feature fusion feedforward neural networks fusion strategies gesture classification gesture spotting graph convolution hand gesture recognition hand shape features high-order feature human action recognition human activity recognition human-computer interaction human-machine interface Long Short-Term Memory multi-modal features multi-modalities network multi-person pose estimation n/a partition pose representation partitioned centerpose network pose estimation real-time spatio-temporal differential spatio-temporal feature spatio-temporal image formation spatiotemporal activations spatiotemporal feature stacked hourglass network transfer learning |
| 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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