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Relevant bibliographies by topics / Deep Learning Imaging / Books

Books on the topic 'Deep Learning Imaging'

To see the other types of publications on this topic, follow the link: Deep Learning Imaging.

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Jain, Lakhmi C., Roumen Kountchev, Yonghang Tai, and Roumiana Kountcheva, eds. 3D Imaging—Multidimensional Signal Processing and Deep Learning. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2452-1.

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2

Jain, Lakhmi C., Roumen Kountchev, and Yonghang Tai, eds. 3D Imaging Technologies—Multidimensional Signal Processing and Deep Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3180-1.

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3

Jain, Lakhmi C., Roumen Kountchev, and Junsheng Shi, eds. 3D Imaging Technologies—Multi-dimensional Signal Processing and Deep Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3391-1.

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4

Lu, Le, Xiaosong Wang, Gustavo Carneiro, and Lin Yang, eds. Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13969-8.

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5

SURI, Biswas. Multimodality Imaging: Deep Learning A. Institute of Physics Publishing, 2022.

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6

Paul, Sudip, and Sanjay Saxena. Deep Learning Applications in Medical Imaging. IGI Global, 2020.

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7

Paul, Sudip, and Sanjay Saxena. Deep Learning Applications in Medical Imaging. IGI Global, 2020.

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8

Deep Learning Models for Medical Imaging. Elsevier, 2022. http://dx.doi.org/10.1016/c2020-0-00344-0.

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9

Das, Nibaran, K. C. Santosh, and Swarnendu Ghosh. Deep Learning Models for Medical Imaging. Elsevier Science & Technology Books, 2021.

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10

Paul, Sudip, and Sanjay Saxena. Deep Learning Applications in Medical Imaging. IGI Global, 2020.

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11

Das, Nibaran, K. C. Santosh, and Swarnendu Ghosh. Deep Learning Models for Medical Imaging. Elsevier Science & Technology, 2021.

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12

Paul, Sudip, and Sanjay Saxena. Deep Learning Applications in Medical Imaging. IGI Global, 2020.

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13

Machine Learning and Deep Learning in Biomedical Imaging. Taylor & Francis Group, 2021.

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14

Zhou, S. Kevin, Hayit Greenspan, and Dinggang Shen. Deep Learning for Medical Image Analysis. Elsevier Science & Technology Books, 2017.

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15

Zhou, Kevin, Hayit Greenspan, and Dinggang Shen. Deep Learning for Medical Image Analysis. Elsevier Science & Technology Books, 2021.

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16

Zhou, Kevin, Hayit Greenspan, and Dinggang Shen. Deep Learning for Medical Image Analysis. Elsevier Science & Technology Books, 2017.

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17

Wang, Xiaosong, Lin Yang, Le Lu, and Gustavo Carneiro. Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Springer International Publishing AG, 2020.

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18

Wang, Xiaosong, Lin Yang, Le Lu, and Gustavo Carneiro. Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Springer, 2019.

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19

Suri, Jasjit S., and Mainak Biswas. Multimodality Imaging of the Heart, Lungs and Peripheral Organs: Deep Learning Applications. Iop Publishing Ltd, 2021.

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20

3D Imaging Technologies--Multidimensional Signal Processing and Deep Learning: Methods, Algorithms and Applications, Volume 2. Springer, 2022.

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21

Jain, Lakhmi C., Roumen Kountchev, and Junsheng Shi. 3D Imaging Technologies - Multidimensional Signal Processing and Deep Learning: Mathematical Approaches and Applications, Volume 1. Springer Singapore Pte. Limited, 2021.

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22

Jain, Lakhmi C., Roumen Kountchev, and Yonghang Tai. 3D Imaging Technologies - Multidimensional Signal Processing and Deep Learning: Methods, Algorithms and Applications, Volume 2. Springer Singapore Pte. Limited, 2021.

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23

Jain, Lakhmi, Roumen Kountchev, Yonghang Tai, and Roumiana Kountcheva. 3D Imaging - Multidimensional Signal Processing and Deep Learning: 3D Images, Graphics and Information Technologies, Volume 1. Springer, 2022.

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24

Jain, Lakhmi C., Roumen Kountchev, and Junsheng Shi. 3D Imaging Technologies--Multi-Dimensional Signal Processing and Deep Learning: Mathematical Approaches and Applications, Volume 1. Springer, 2022.

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25

Jain, Lakhmi, Roumen Kountchev, Yonghang Tai, and Roumiana Kountcheva. 3D Imaging - Multidimensional Signal Processing and Deep Learning: Multidimensional Signals, Images, Video Processing and Applications, Volume 2. Springer, 2022.

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26

Bruno, Michael A. Error and Uncertainty in Diagnostic Radiology. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190665395.001.0001.

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Diagnostic radiology is a medical specialty that is primarily devoted to the diagnostic process, centered on the interpretation of medical images. This book reviews the high level of uncertainty inherent to radiological interpretation and the overlap that exists between the uncertainty of the process and what might be considered “error.” There is also a great deal of variability inherent in the physical and technological aspects of the imaging process itself. The information in diagnostic images is subtly encoded, with a broad range of “normal” that usually overlaps the even broader range of “abnormal.” Image interpretation thus blends technology, medical science, and human intuition. To develop their skillset, radiologists train intensively for years, and most develop a remarkable level of expertise. But radiology itself remains a fallible human endeavor, one involving complex neurophysiological and cognitive processes employed under a range of conditions and generally performed under time pressure. This book highlights the human experience of error. A taxonomy of error is presented, along with a theoretical classification of error types based on the underlying causes and an extensive discussion of potential error-reduction strategies. The relevant perceptual science, cognitive science, and imaging science are reviewed. A chapter addresses the issue of accountability for error, including peer review, regulatory oversight/accreditation, and malpractice litigation. The potential impact of artificial intelligence, including the use of machine learning and deep-learning algorithms, to reduce human error and improve radiologists’ efficiency is also explored.

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27

Delgado Martín, Jordi, Andrea Muñoz-Ibáñez, and Ismael Himar Falcón-Suárez. 6th International Workshop on Rock Physics: A Coruña, Spain 13 -17 June 2022: Book of Abstracts. 2022nd ed. Servizo de Publicacións da UDC, 2022. http://dx.doi.org/10.17979/spudc.000005.

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[Abstract] The 6th International Workshop on Rock Physics (6IWRP) was held A Coruña, Spain, between 13th and 17th of June, 2022. This meeting follows the track of the five successful encounters held in Golden (USA, 2011), Southampton (UK, 2013), Perth (Australia, 2015), Trondheim (Norway, 2017) and Hong Kong (China, 2019). The aim of the workshop was to bring together experiences allowing to illustrate, discuss and exchange recent advances in the wide realm of rock physics, including theoretical developments, in situ and laboratory scale experiments as well as digital analysis. While rock physics is at the core of the oil & gas industry applications, it is also essential to enable the energy transition challenge (e.g. CO2 and H2 storage, geothermal), ensure a safe and adequate use of natural resources and develop efficient waste management strategies. The topics of 6IWRP covered a broad spectrum of rock physics-related research activities, including: • Experimental rock physics. New techniques, approaches and applications; Characterization of the static and dynamic properties of rocks and fluids; Multiphysics measurements (NMR, electrical resistivity…); Deep/crustal scale rock physics. • Modelling and multiscale applications: from the lab to the field. Numerical analysis and model development; Data science applications; Upscaling; Microseismicity and earthquakes; Subsurface stresses and tectonic deformations. • Coupled phenomena and rock properties: exploring interactions. Anisotropy; Flow and fractures; Temperature effects; Rock-fluid interaction; Fluid and pressure effects on geophysical signatures. • The energy transition challenge. Applications to energy storage (hydrogen storage in porous media), geothermal resources, energy production (gas hydrates), geological utilization and storage of CO2, nuclear waste disposal. • Rock physics templates: advances and applications. Quantitative assessment; Applications to reser voir characterization (role of seismic wave anisotropy and fracture networks). • Advanced rock physics tools. Machine learning; application of imaging (X-ray CT, X-ray μCT, FIB-SEM…) to obtain rock proper ties. This book compiles more than 50 abstracts, summarizing the works presented in the 6IWRP by rock physicists from all over the world, belonging to both academia and industry. This book means an updated overview of the rock physics research worldwide.

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