國立虎尾科技大學 |

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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Data-driven clinical decision-making using deep learning in imaging/ edited by M. F. Mridha, Nilanjan Dey.
other author:	Mridha, M. F.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xii, 274 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Diagnostic imaging - Data processing. -
Online resource:	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.1..

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:

678955
Diagnostic imaging
--Data processing.

LC Class. No.: RC78.7.D53

Dewey Class. No.: 616.0754

Data-driven clinical decision-making using deep learning in imaging
LDR:04066nam a22003495a 4500 001 1135097
003 DE-He213
005 20240814130240.0
006 m d
007 cr nn 008maaau
008 241213s2024 si s 0 eng d
020 $a 9789819739660 $q (electronic bk.)
020 $a 9789819739653 $q (paper)
024 7 $a 10.1007/978-981-97-3966-0 $2 doi
035 $a 978-981-97-3966-0
040 $a GP $c GP
041 0 $a eng
050 4 $a RC78.7.D53
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 616.0754 $2 23
090 $a RC78.7.D53 $b D232 2024
245 0 0 $a Data-driven clinical decision-making using deep learning in imaging $h [electronic resource] / $c edited by M. F. Mridha, Nilanjan Dey.
260 $a Singapore : $c 2024. $b Springer Nature Singapore : $b Imprint: Springer,
300 $a xii, 274 p. : $b ill., digital ; $c 24 cm.
347 $a text file $b PDF $2 rda
490 1 $a Studies in big data, $x 2197-6511 ; $v v. 152
505 0 $a 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.
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.
650 0 $a Diagnostic imaging $x Data processing. $3 678955
650 0 $a Diagnosis $x Decision making $x Data processing. $3 791340
650 0 $a Deep learning (Machine learning) $3 1381171
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Optimization. $3 669174
700 1 $a Mridha, M. F. $3 1456719
700 1 $a Dey, Nilanjan. $3 1110380
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Studies in big data ; $v v.1. $3 1020233
856 4 0 $u https://doi.org/10.1007/978-981-97-3966-0
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)