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...

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Библиографические подробности
Другие авторы:	Rundo, Leonardo (Редактор), Militello, Carmelo (Редактор), Conti, Vincenzo (Редактор), Zaccagna, Fulvio (Редактор), Han, Changhee (Редактор)
Формат:	Глава книги
Опубликовано:	Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Предметы:	Medicine 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
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Итог:	[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.]
Объем:	1 electronic resource (262 p.)
ISBN:	books978-3-0365-2555-6 9783036525549 9783036525556
Доступ:	Open Access

Advanced Computational Methods for Oncological Image Analysis

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