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Data driven approaches on medical imaging

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data driven approaches on medical imaging/ edited by Bin Zheng ... [et al.].
其他作者:	Zheng, Bin.
出版者:	Cham :Springer Nature Switzerland : : 2023.,
面頁冊數:	xv, 228 p. :ill. (some col.), digital ;24 cm.
內容註:	Chapter. 1. Introduction of Medical Imaging Modalities -- Chapter. 2. Introduction to Medical Imaging Informatics -- Chapter. 3. Active Learning on Medical Image -- Chapter. 4. Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework -- Chapter. 5. AUTOML Systems for Medical Imaging -- Chapter. 6. Online learning for X-ray, CT or MRI -- Chapter. 7. Invariant Scattering Transform for Medical Imaging -- Chapter. 8. Generative Adversarial Networks for Data Augmentation -- Chapter. 9. Bias, Ethical concerns, and explainable decision-making in medical imaging research -- Chapter. 10. Case Studies on X-Ray Imaging, MRI and Nuclear Imaging -- Index.
Contained By:	Springer Nature eBook
標題:	Diagnostic imaging - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-031-47772-0
ISBN:	9783031477720

Data driven approaches on medical imaging
Data driven approaches on medical imaging[electronic resource] /edited by Bin Zheng ... [et al.]. - Cham :Springer Nature Switzerland :2023. - xv, 228 p. :ill. (some col.), digital ;24 cm.

Chapter. 1. Introduction of Medical Imaging Modalities -- Chapter. 2. Introduction to Medical Imaging Informatics -- Chapter. 3. Active Learning on Medical Image -- Chapter. 4. Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework -- Chapter. 5. AUTOML Systems for Medical Imaging -- Chapter. 6. Online learning for X-ray, CT or MRI -- Chapter. 7. Invariant Scattering Transform for Medical Imaging -- Chapter. 8. Generative Adversarial Networks for Data Augmentation -- Chapter. 9. Bias, Ethical concerns, and explainable decision-making in medical imaging research -- Chapter. 10. Case Studies on X-Ray Imaging, MRI and Nuclear Imaging -- Index.

This book deals with the recent advancements in computer vision techniques such as active learning, few-shot learning, zero shot learning, explainable and interpretable ML, online learning, AutoML etc. and their applications in medical domain. Moreover, the key challenges which affect the design, development, and performance of medical imaging systems are addressed. In addition, the state-of-the-art medical imaging methodologies for efficient, interpretable, explainable, and practical implementation of computer imaging techniques are discussed. At present, there are no textbook resources that address the medical imaging technologies. There are ongoing and novel research outcomes which would be useful for the development of novel medical imaging technologies/processes/equipment which can improve the current state of the art. The book particularly focuses on the use of data driven new technologies on medical imaging vision such as Active learning, Online learning, few shot learning, AutoML, segmentation etc.

ISBN: 9783031477720

Standard No.: 10.1007/978-3-031-47772-0doiSubjects--Topical Terms:

817500
Diagnostic imaging
--Data processing.

LC Class. No.: RC78.7.D53

Dewey Class. No.: 616.0754

Data driven approaches on medical imaging
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505 0 $a Chapter. 1. Introduction of Medical Imaging Modalities -- Chapter. 2. Introduction to Medical Imaging Informatics -- Chapter. 3. Active Learning on Medical Image -- Chapter. 4. Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework -- Chapter. 5. AUTOML Systems for Medical Imaging -- Chapter. 6. Online learning for X-ray, CT or MRI -- Chapter. 7. Invariant Scattering Transform for Medical Imaging -- Chapter. 8. Generative Adversarial Networks for Data Augmentation -- Chapter. 9. Bias, Ethical concerns, and explainable decision-making in medical imaging research -- Chapter. 10. Case Studies on X-Ray Imaging, MRI and Nuclear Imaging -- Index.
520 $a This book deals with the recent advancements in computer vision techniques such as active learning, few-shot learning, zero shot learning, explainable and interpretable ML, online learning, AutoML etc. and their applications in medical domain. Moreover, the key challenges which affect the design, development, and performance of medical imaging systems are addressed. In addition, the state-of-the-art medical imaging methodologies for efficient, interpretable, explainable, and practical implementation of computer imaging techniques are discussed. At present, there are no textbook resources that address the medical imaging technologies. There are ongoing and novel research outcomes which would be useful for the development of novel medical imaging technologies/processes/equipment which can improve the current state of the art. The book particularly focuses on the use of data driven new technologies on medical imaging vision such as Active learning, Online learning, few shot learning, AutoML, segmentation etc.
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