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Data-driven clinical decision-making...

Mridha, M. F.

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Data-driven clinical decision-making using deep learning in imaging

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-driven clinical decision-making using deep learning in imaging/ edited by M. F. Mridha, Nilanjan Dey.
其他作者:	Mridha, M. F.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xii, 274 p. :ill., digital ;24 cm.
內容註:	Improved 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.
Contained By:	Springer Nature eBook
標題:	Diagnostic imaging - Data processing. -
電子資源:	https://doi.org/10.1007/978-981-97-3966-0
ISBN:	9789819739660

Data-driven clinical decision-making using deep learning in imaging
Data-driven clinical decision-making using deep learning in imaging[electronic resource] /edited by M. F. Mridha, Nilanjan Dey. - Singapore :Springer Nature Singapore :2024. - xii, 274 p. :ill., digital ;24 cm. - Studies in big data,v. 1522197-6511 ;. - Studies in big data ;v. 152..

Improved 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.

This 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.

ISBN: 9789819739660

Standard No.: 10.1007/978-981-97-3966-0doiSubjects--Topical Terms:

817500
Diagnostic imaging
--Data processing.

LC Class. No.: RC78.7.D53

Dewey Class. No.: 616.0754

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520 $a This 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.
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