Advanced Computational Methods for Oncological Image Analysis
[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. T...
Shranjeno v:
| Format: | Online |
|---|---|
| Jezik: | angleščina |
| Izdano: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
|
| Teme: | |
| Online dostop: | ONIX_20220111_9783036525549_875 |
| Oznake: |
Brez oznak, prvi označite!
|
| _version_ | 1869528791854874624 |
|---|---|
| collection | Directory of Open Access Books |
| description | [Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.] |
| format | Online |
| id | doab-20.500.12854ir-77043 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-770432024-03-31T13:10:01Z Advanced Computational Methods for Oncological Image Analysis Rundo, Leonardo Militello, Carmelo Conti, Vincenzo Zaccagna, Fulvio Han, Changhee 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 n/a thema EDItEUR::M Medicine and Nursing [Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.] 2022-01-11T13:50:17Z 2022-01-11T13:50:17Z 2021 book ONIX_20220111_9783036525549_875 9783036525549 9783036525556 https://directory.doabooks.org/handle/20.500.12854/77043 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/4653 https://mdpi.com/books/pdfview/book/4653 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-2555-6 10.3390/books978-3-0365-2555-6 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036525549 9783036525556 262 Basel, Switzerland open access |
| spellingShingle | 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 n/a thema EDItEUR::M Medicine and Nursing Advanced Computational Methods for Oncological Image Analysis |
| title | Advanced Computational Methods for Oncological Image Analysis |
| title_full | Advanced Computational Methods for Oncological Image Analysis |
| title_fullStr | Advanced Computational Methods for Oncological Image Analysis |
| title_full_unstemmed | Advanced Computational Methods for Oncological Image Analysis |
| title_short | Advanced Computational Methods for Oncological Image Analysis |
| title_sort | advanced computational methods for oncological image analysis |
| topic | 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 n/a thema EDItEUR::M Medicine and Nursing |
| topic_facet | 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 n/a thema EDItEUR::M Medicine and Nursing |
| url | ONIX_20220111_9783036525549_875 |