000			03191nam a22003375i 4500
999			_c403023 _d403023
001			403023
003			ES-MaUEC
005			20240701123330.0
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007			cr nn 008mamaa
008			240224s2023 sz | o |||| 0|eng d
020			_a9783031484469
024	7		_a10.1007/978-3-031-48446-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRC78.7 .D53 _b2023 EB
245	0	0	_aBasics of Image Processing : _bThe Facts and Challenges of Data Harmonization to Improve Radiomics Reproducibility _cedited by Ángel Alberich-Bayarri, Fuensanta Bellvís-Bataller
250			_a1st ed. 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
490	0		_aImaging Informatics for Healthcare Professionals _x2662-155X
505	0		_aEra of AI quantitative imaging -- Principles of image formation in the different modalities -- How to extract radiomic features from the image? -- Facts and needs to improve Radiomics reproductibility -- What is harmonization and how does it differ from standardization? -- Harmonization in the image domain -- Harmonization across MRI -- Harmonization in the features domain -- Selection of the optimal harmonization method(s) for the problem under study -- Conclusions.
520			_aThis book, endorsed by EuSoMii, provides clinicians, researchers and scientists a useful handbook to navigate the intricate landscape of data harmonization, as we embark on a journey to improve the reproducibility, robustness and generalizability of multi-centric real-world data radiomic studies. In these pages, the authors delve into the foundational principles of radiomics and its far-reaching implications for precision medicine. They describe the different methodologies used in extracting quantitative features from medical images, the building blocks that enable the transformation of images into actionable predictions. This book sweeps from understanding the basis of harmonization to the implementation of all the knowledge acquired to date, with the aim of conveying the importance of harmonizing medical data and providing a useful guidance to enable its applicability and the future use of advanced radiomics-based models in routine clinical practice. As authors embark on this exploration of data harmonization in radiomics, they hope to ignite discussions, foster new ideas, and inspire researchers, clinicians, and scientists alike to embrace the challenges and opportunities that lie ahead. Together, they elevate radiomics as a reproducible technology and establish it as an indispensable and actionable tool in the quest for improved cancer diagnosis and treatment.
988			_aSpringer_Medicine_2023
650		7	_2embne _9305596 _aDiagnóstico por imagen _xTécnicas digitales
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-48446-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b07/2024 _dz _eu _zSI