Deep Neural Networks in Medical Imaging
Medical Imaging plays a key role in disease management, starting from baseline risk assessment, diagnosis, staging, therapy planning, therapy delivery, and follow-up. Each type of disease has led to the development of more advanced imaging methods and modalities to help clinicians address the specif...
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| Ձևաչափ: | Online |
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| Լեզու: | անգլերեն |
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MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Առցանց հասանելիություն: | ONIX_20250220_9783725825257_316 |
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Չկան պիտակներ, Եղեք առաջինը, ով նշում է այս գրառումը!
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| _version_ | 1869517308317138944 |
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| collection | Directory of Open Access Books |
| description | Medical Imaging plays a key role in disease management, starting from baseline risk assessment, diagnosis, staging, therapy planning, therapy delivery, and follow-up. Each type of disease has led to the development of more advanced imaging methods and modalities to help clinicians address the specific challenges in analyzing the underlying disease mechanisms. Researchers have been actively pursuing the development of advanced image analysis algorithms. These developments were driven by the need for a comprehensive quantification of structure and function across several imaging modalities such as Computed Tomography (CT), X-ray Radiography, Magnetic Resonance Imaging (MRI), Ultrasound, Nuclear Medicine Imaging, and Digital Pathology. Currently, deep learning has become the state-of-the-art machine learning technique, providing unprecedented performance for learning patterns in medical images and great promise for helping physicians during clinical decision-making processes. The aim of this work is to present and highlight novel methods, architectures, techniques, and applications of deep learning in medical imaging related to, but not limited to, the following topics: image reconstruction; image enhancement; segmentation; registration; computer-aided detection; image or view recognition; multi-task learning; transfer learning; generative learning; self-supervised learning; semi-supervised learning; weakly supervised learning; unsupervised learning; privacy preserving learning; explainability and interpretability; and robustness and out-of-distribution detection. |
| format | Online |
| id | doab-20.500.12854ir-152952 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1529522025-02-20T13:20:11Z Deep Neural Networks in Medical Imaging Itu, Lucian Mihai Suciu, Constantin Vizitiu, Anamaria Image reconstruction image enhancement image generation segmentation registration computer aided detection weakly supervised learning privacy preserving learning explainability and interpretability robustness and out-of-distribution detection cardiovascular diseases thema EDItEUR::M Medicine and Nursing thema EDItEUR::M Medicine and Nursing::MN Surgery Medical Imaging plays a key role in disease management, starting from baseline risk assessment, diagnosis, staging, therapy planning, therapy delivery, and follow-up. Each type of disease has led to the development of more advanced imaging methods and modalities to help clinicians address the specific challenges in analyzing the underlying disease mechanisms. Researchers have been actively pursuing the development of advanced image analysis algorithms. These developments were driven by the need for a comprehensive quantification of structure and function across several imaging modalities such as Computed Tomography (CT), X-ray Radiography, Magnetic Resonance Imaging (MRI), Ultrasound, Nuclear Medicine Imaging, and Digital Pathology. Currently, deep learning has become the state-of-the-art machine learning technique, providing unprecedented performance for learning patterns in medical images and great promise for helping physicians during clinical decision-making processes. The aim of this work is to present and highlight novel methods, architectures, techniques, and applications of deep learning in medical imaging related to, but not limited to, the following topics: image reconstruction; image enhancement; segmentation; registration; computer-aided detection; image or view recognition; multi-task learning; transfer learning; generative learning; self-supervised learning; semi-supervised learning; weakly supervised learning; unsupervised learning; privacy preserving learning; explainability and interpretability; and robustness and out-of-distribution detection. 2025-02-20T13:20:09Z 2025-02-20T13:20:09Z 2024 book ONIX_20250220_9783725825257_316 9783725825257 9783725825264 https://directory.doabooks.org/handle/20.500.12854/152952 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/10126 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-2526-4 10.3390/books978-3-7258-2526-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725825257 9783725825264 250 Basel open access |
| spellingShingle | Image reconstruction image enhancement image generation segmentation registration computer aided detection weakly supervised learning privacy preserving learning explainability and interpretability robustness and out-of-distribution detection cardiovascular diseases thema EDItEUR::M Medicine and Nursing thema EDItEUR::M Medicine and Nursing::MN Surgery Deep Neural Networks in Medical Imaging |
| title | Deep Neural Networks in Medical Imaging |
| title_full | Deep Neural Networks in Medical Imaging |
| title_fullStr | Deep Neural Networks in Medical Imaging |
| title_full_unstemmed | Deep Neural Networks in Medical Imaging |
| title_short | Deep Neural Networks in Medical Imaging |
| title_sort | deep neural networks in medical imaging |
| topic | Image reconstruction image enhancement image generation segmentation registration computer aided detection weakly supervised learning privacy preserving learning explainability and interpretability robustness and out-of-distribution detection cardiovascular diseases thema EDItEUR::M Medicine and Nursing thema EDItEUR::M Medicine and Nursing::MN Surgery |
| topic_facet | Image reconstruction image enhancement image generation segmentation registration computer aided detection weakly supervised learning privacy preserving learning explainability and interpretability robustness and out-of-distribution detection cardiovascular diseases thema EDItEUR::M Medicine and Nursing thema EDItEUR::M Medicine and Nursing::MN Surgery |
| url | ONIX_20250220_9783725825257_316 |