Deep Learning for Pathology Detection and Diagnosis in Medical Imaging
Severe pathologies, such as the diffuse liver diseases or tumors, can lead to the significant degradation of the human health and sometimes to lethal stages. The most reliable methods for the diagnosis of these affections, such as the classical biopsy or surgery, are invasive and dangerous. Advanced...
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| Format: | Online |
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| Idioma: | anglès |
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MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Accés en línia: | ONIX_20250220_9783725820412_85 |
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| _version_ | 1869518916105011200 |
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| collection | Directory of Open Access Books |
| description | Severe pathologies, such as the diffuse liver diseases or tumors, can lead to the significant degradation of the human health and sometimes to lethal stages. The most reliable methods for the diagnosis of these affections, such as the classical biopsy or surgery, are invasive and dangerous. Advanced computerized methods are urgently needed to reduce invasiveness and enhance the information derived from medical images as much as possible by unveiling their subtle aspects, conducting to a virtual biopsy. Computer Vision and Machine Learning can be successfully employed to achieve this target. Thus, advanced image analysis combined with conventional machine learning, as well as the deep learning techniques, can lead to a highly accurate automatic diagnosis process. The corresponding features, together with the classification, segmentation, fusion of multiple image modalities, and 3D reconstruction techniques, can be involved in the achievement of appropriate 2D and 3D models for the considered affections, which are helpful in computer-aided diagnosis and surgery. The purpose of the special issue “Deep Learning for Pathology Detection and Diagnosis in Medical Imaging” is that of offering the opportunity to disseminate valuable and original results achieved in the corresponding field, surprising the latest, deep-learning techniques, eventually compared and combined with conventional methods. |
| format | Online |
| id | doab-20.500.12854ir-152721 |
| 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-1527212025-02-20T12:58:01Z Deep Learning for Pathology Detection and Diagnosis in Medical Imaging Nedevschi, Sergiu Mitrea, Delia-Alexandrina virtual biopsy severe pathology computer vision deep learning radiomics automatic and computer-aided diagnosis medical images thema EDItEUR::M Medicine and Nursing Severe pathologies, such as the diffuse liver diseases or tumors, can lead to the significant degradation of the human health and sometimes to lethal stages. The most reliable methods for the diagnosis of these affections, such as the classical biopsy or surgery, are invasive and dangerous. Advanced computerized methods are urgently needed to reduce invasiveness and enhance the information derived from medical images as much as possible by unveiling their subtle aspects, conducting to a virtual biopsy. Computer Vision and Machine Learning can be successfully employed to achieve this target. Thus, advanced image analysis combined with conventional machine learning, as well as the deep learning techniques, can lead to a highly accurate automatic diagnosis process. The corresponding features, together with the classification, segmentation, fusion of multiple image modalities, and 3D reconstruction techniques, can be involved in the achievement of appropriate 2D and 3D models for the considered affections, which are helpful in computer-aided diagnosis and surgery. The purpose of the special issue “Deep Learning for Pathology Detection and Diagnosis in Medical Imaging” is that of offering the opportunity to disseminate valuable and original results achieved in the corresponding field, surprising the latest, deep-learning techniques, eventually compared and combined with conventional methods. 2025-02-20T12:57:58Z 2025-02-20T12:57:58Z 2024 book ONIX_20250220_9783725820412_85 9783725820412 9783725820429 https://directory.doabooks.org/handle/20.500.12854/152721 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/9901 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-2042-9 10.3390/books978-3-7258-2042-9 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725820412 9783725820429 220 Basel open access |
| spellingShingle | virtual biopsy severe pathology computer vision deep learning radiomics automatic and computer-aided diagnosis medical images thema EDItEUR::M Medicine and Nursing Deep Learning for Pathology Detection and Diagnosis in Medical Imaging |
| title | Deep Learning for Pathology Detection and Diagnosis in Medical Imaging |
| title_full | Deep Learning for Pathology Detection and Diagnosis in Medical Imaging |
| title_fullStr | Deep Learning for Pathology Detection and Diagnosis in Medical Imaging |
| title_full_unstemmed | Deep Learning for Pathology Detection and Diagnosis in Medical Imaging |
| title_short | Deep Learning for Pathology Detection and Diagnosis in Medical Imaging |
| title_sort | deep learning for pathology detection and diagnosis in medical imaging |
| topic | virtual biopsy severe pathology computer vision deep learning radiomics automatic and computer-aided diagnosis medical images thema EDItEUR::M Medicine and Nursing |
| topic_facet | virtual biopsy severe pathology computer vision deep learning radiomics automatic and computer-aided diagnosis medical images thema EDItEUR::M Medicine and Nursing |
| url | ONIX_20250220_9783725820412_85 |