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245	1	0	_aData-Driven Clinical Decision-Making Using Deep Learning in Imaging _h[electronic resource] / _cedited by M. F. Mridha, Nilanjan Dey.
250			_a1st ed. 2024.
264		1	_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2024.
300			_aXII, 274 p. 105 illus., 92 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStudies in Big Data, _x2197-6511 ; _v152
505	0		_aImproved Classification of Kidney Lesions in CT scans using CNN with Attention Layers: Achieving High Accuracy and Performance -- Domain Adaptation in Medical Imaging: Evaluating the Effectiveness of Transfer Learning -- Elevating Breast Cancer Research: Discovering New Frontiers with Attention-Based U-Net Architecture for Segmentation -- Early Skin Cancer Detection in Computer Vision: Leveraging Attention-Based Deep Ensembles -- Incorporating Residual Connections into a Multi-Channel CNN for Interpretable Lung Cancer Detection in Digital Pathology -- Privacy Preserving Breast Cancer Risk Prediction with Mammography Images Using Federated Learning -- Federated Learning for Scabies Recognition: A Privacy-Preserving Approach -- An Improved Transfer Learning based Approach for the Classification of Multi-Stage HER2 Breast Cancer from Hematoxylin and Eosin Images -- Unveiling the Unique Dermatological Signatures of Human Monkeypox, Chickenpox, and Measles through Deep Transfer Learning Model -- Development of a Deep Learning Framework for Brain Tumors Classification Using Transfer Learning -- Featured-based brain tumor image registration using a Fussy-clustering segmentation approach -- Enhancing Breast Cancer Detection Systems: Augmenting and Upscaling Mammogram Images using Generative Adversarial Networks -- A Deep Learning Approach Bone Marrow Cancer Cell Multiclass Classification using Microscopic Images -- Detecting Skin Cancer Through the Utilization of Deep Convolutional Neural Networks and Generative Adversarial Networks.
520			_aThis book explores cutting-edge medical imaging advancements and their applications in clinical decision-making. The book contains various topics, methodologies, and applications, providing readers with a comprehensive understanding of the field's current state and prospects. It begins with exploring domain adaptation in medical imaging and evaluating the effectiveness of transfer learning to overcome challenges associated with limited labeled data. The subsequent chapters delve into specific applications, such as improving kidney lesion classification in CT scans, elevating breast cancer research through attention-based U-Net architecture for segmentation and classifying brain MRI images for neurological disorders. Furthermore, the book addresses the development of multimodal machine learning models for brain tumor prognosis, the identification of unique dermatological signatures using deep transfer learning, and the utilization of generative adversarial networks to enhance breast cancer detection systems by augmenting mammogram images. Additionally, the authors present a privacy-preserving approach for breast cancer risk prediction using federated learning, ensuring the confidentiality and security of sensitive patient data. This book brings together a global network of experts from various corners of the world, reflecting the truly international nature of its research.
541			_fUABC ; _cPerpetuidad
650		0	_aComputational intelligence.
650		0	_aMachine learning.
650		0	_aMathematical optimization.
650	1	4	_aComputational Intelligence.
650	2	4	_aMachine Learning.
650	2	4	_aOptimization.
700	1		_aMridha, M. F. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aDey, Nilanjan. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789819739653
776	0	8	_iPrinted edition: _z9789819739677
776	0	8	_iPrinted edition: _z9789819739684
830		0	_aStudies in Big Data, _x2197-6511 ; _v152
856	4	0	_zLibro electrónico _uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-97-3966-0
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