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Relevant bibliographies by topics / Deep Learning Imaging / Journal articles

Journal articles 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

Nizami Huseyn, Elcin. "APPLICATION OF DEEP LEARNING IN MEDICAL IMAGING." NATURE AND SCIENCE 03, no. 04 (October 27, 2020): 7–13. http://dx.doi.org/10.36719/2707-1146/04/7-13.

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Medical imaging technology plays an important role in the detection, diagnosis and treatment of diseases. Due to the instability of human expert experience, machine learning technology is expected to assist researchers and physicians to improve the accuracy of imaging diagnosis and reduce the imbalance of medical resources. This article systematically summarizes some methods of deep learning technology, introduces the application research of deep learning technology in medical imaging, and discusses the limitations of deep learning technology in medical imaging. Key words: Artificial Intelligence, Deep Learning, Medical Imaging, big data

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2

Wang, Weihao, Xing Zhao, Zhixiang Jiang, and Ya Wen. "Deep learning-based scattering removal of light field imaging." Chinese Optics Letters 20, no. 4 (2022): 041101. http://dx.doi.org/10.3788/col202220.041101.

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3

Sengupta, Partho P., and Y. Chandrashekhar. "Imaging With Deep Learning." JACC: Cardiovascular Imaging 15, no. 3 (March 2022): 547–49. http://dx.doi.org/10.1016/j.jcmg.2022.02.001.

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4

Strack, Rita. "Deep learning in imaging." Nature Methods 16, no. 1 (December 20, 2018): 17. http://dx.doi.org/10.1038/s41592-018-0267-9.

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5

Klang, Eyal. "Deep learning and medical imaging." Journal of Thoracic Disease 10, no. 3 (March 2018): 1325–28. http://dx.doi.org/10.21037/jtd.2018.02.76.

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6

van Sloun, Ruud J. G., Regev Cohen, and Yonina C. Eldar. "Deep Learning in Ultrasound Imaging." Proceedings of the IEEE 108, no. 1 (January 2020): 11–29. http://dx.doi.org/10.1109/jproc.2019.2932116.

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7

Kim, Mingyu, Jihye Yun, Yongwon Cho, Keewon Shin, Ryoungwoo Jang, Hyun-jin Bae, and Namkug Kim. "Deep Learning in Medical Imaging." Neurospine 16, no. 4 (December 31, 2019): 657–68. http://dx.doi.org/10.14245/ns.1938396.198.

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8

Kim, Mingyu, Jihye Yun, Yongwon Cho, Keewon Shin, Ryoungwoo Jang, Hyun-jin Bae, and Namkug Kim. "Deep Learning in Medical Imaging." Neurospine 17, no. 2 (June 30, 2020): 471–72. http://dx.doi.org/10.14245/ns.1938396.198.c1.

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9

Liu, Fang, and Richard Kijowski. "Deep Learning in Musculoskeletal Imaging." Advances in Clinical Radiology 1 (September 2019): 83–94. http://dx.doi.org/10.1016/j.yacr.2019.04.013.

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10

Currie, Geoff, K. Elizabeth Hawk, Eric Rohren, Alanna Vial, and Ran Klein. "Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging." Journal of Medical Imaging and Radiation Sciences 50, no. 4 (December 2019): 477–87. http://dx.doi.org/10.1016/j.jmir.2019.09.005.

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11

Singh, Chetanpal. "Medical Imaging using Deep Learning Models." European Journal of Engineering and Technology Research 6, no. 5 (August 23, 2021): 156–67. http://dx.doi.org/10.24018/ejers.2021.6.5.2491.

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Deep learning has played a potential role in quality healthcare with fast automated and proper medical image analysis. In clinical applications, medical imaging is one of the most important parameters as with the help of this; experts can detect, monitor, and diagnose any kind of problems that are there in the patient's body. However, there are two things that one needs to understand; that is, the implementation of Artificial Neural Networks and Convolutional Neural Networks as well as deep learning to know about medical image analysis. It is necessary to state here that the deep learning approach is gaining attention in the medical imaging field in evaluating the presence or absence of disease in a patient. Mammography images, digital histopathology images, computerized tomography, etc. are some of the areas on which DL implementation focuses. One upon going through the paper will get to know the recent development that has occurred in this field and come up with a critical review on this aspect. The paper has demonstrated in detail modern deep learning models that are implemented in medical image analysis. There is no doubt about the promising future of the deep learning models and according to experts; the implementation of deep learning techniques has outperformed medical experts in numerous tasks. However, deep learning also has some drawbacks and challenges that are required to be addressed like limited datasets and many more. To mitigate such kinds of challenges, researchers are working on this aspect so that they can enhance healthcare by deploying AI.

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12

Singh, Chetanpal. "Medical Imaging using Deep Learning Models." European Journal of Engineering and Technology Research 6, no. 5 (August 23, 2021): 156–67. http://dx.doi.org/10.24018/ejeng.2021.6.5.2491.

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Deep learning has played a potential role in quality healthcare with fast automated and proper medical image analysis. In clinical applications, medical imaging is one of the most important parameters as with the help of this; experts can detect, monitor, and diagnose any kind of problems that are there in the patient's body. However, there are two things that one needs to understand; that is, the implementation of Artificial Neural Networks and Convolutional Neural Networks as well as deep learning to know about medical image analysis. It is necessary to state here that the deep learning approach is gaining attention in the medical imaging field in evaluating the presence or absence of disease in a patient. Mammography images, digital histopathology images, computerized tomography, etc. are some of the areas on which DL implementation focuses. One upon going through the paper will get to know the recent development that has occurred in this field and come up with a critical review on this aspect. The paper has demonstrated in detail modern deep learning models that are implemented in medical image analysis. There is no doubt about the promising future of the deep learning models and according to experts; the implementation of deep learning techniques has outperformed medical experts in numerous tasks. However, deep learning also has some drawbacks and challenges that are required to be addressed like limited datasets and many more. To mitigate such kinds of challenges, researchers are working on this aspect so that they can enhance healthcare by deploying AI.

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13

Strack, Rita. "Deep learning advances super-resolution imaging." Nature Methods 15, no. 6 (May 31, 2018): 403. http://dx.doi.org/10.1038/s41592-018-0028-9.

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14

Ziatdinov, Maxim, Ondrej Dyck, Stephen Jesse, and Sergei V. Kalinin. "Deep Learning for Atomically Resolved Imaging." Microscopy and Microanalysis 24, S1 (August 2018): 60–61. http://dx.doi.org/10.1017/s143192761800079x.

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15

Yuan, Xin, and Yunchen Pu. "Deep Learning for Lensless Compressive Imaging." Microscopy and Microanalysis 24, S1 (August 2018): 506–7. http://dx.doi.org/10.1017/s1431927618003021.

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16

Wu, Heng, Ruizhou Wang, Genping Zhao, Huapan Xiao, Jian Liang, Daodang Wang, Xiaobo Tian, Lianglun Cheng, and Xianmin Zhang. "Deep-learning denoising computational ghost imaging." Optics and Lasers in Engineering 134 (November 2020): 106183. http://dx.doi.org/10.1016/j.optlaseng.2020.106183.

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17

Shimobaba, Tomoyoshi, Yutaka Endo, Takashi Nishitsuji, Takayuki Takahashi, Yuki Nagahama, Satoki Hasegawa, Marie Sano, et al. "Computational ghost imaging using deep learning." Optics Communications 413 (April 2018): 147–51. http://dx.doi.org/10.1016/j.optcom.2017.12.041.

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18

Kovács, Péter, Bernhard Lehner, Gregor Thummerer, Günther Mayr, Peter Burgholzer, and Mario Huemer. "Deep learning approaches for thermographic imaging." Journal of Applied Physics 128, no. 15 (October 21, 2020): 155103. http://dx.doi.org/10.1063/5.0020404.

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19

Sinha, Ayan, Justin Lee, Shuai Li, and George Barbastathis. "Lensless computational imaging through deep learning." Optica 4, no. 9 (September 15, 2017): 1117. http://dx.doi.org/10.1364/optica.4.001117.

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20

Huang, Kangrui, Hiroki Matsumura, Yaqi Zhao, Maik Herbig, Dan Yuan, Yohei Mineharu, Jeffrey Harmon, et al. "Deep imaging flow cytometry." Lab on a Chip 22, no. 5 (2022): 876–89. http://dx.doi.org/10.1039/d1lc01043c.

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21

Currie, Geoff. "Intelligent Imaging: Anatomy of Machine Learning and Deep Learning." Journal of Nuclear Medicine Technology 47, no. 4 (August 10, 2019): 273–81. http://dx.doi.org/10.2967/jnmt.119.232470.

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22

Ahmad, Hafiz Mughees, Muhammad Jaleed Khan, Adeel Yousaf, Sajid Ghuffar, and Khurram Khurshid. "Deep Learning: A Breakthrough in Medical Imaging." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 8 (October 19, 2020): 946–56. http://dx.doi.org/10.2174/1573405615666191219100824.

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Deep learning has attracted great attention in the medical imaging community as a promising solution for automated, fast and accurate medical image analysis, which is mandatory for quality healthcare. Convolutional neural networks and its variants have become the most preferred and widely used deep learning models in medical image analysis. In this paper, concise overviews of the modern deep learning models applied in medical image analysis are provided and the key tasks performed by deep learning models, i.e. classification, segmentation, retrieval, detection, and registration are reviewed in detail. Some recent researches have shown that deep learning models can outperform medical experts in certain tasks. With the significant breakthroughs made by deep learning methods, it is expected that patients will soon be able to safely and conveniently interact with AI-based medical systems and such intelligent systems will actually improve patient healthcare. There are various complexities and challenges involved in deep learning-based medical image analysis, such as limited datasets. But researchers are actively working in this area to mitigate these challenges and further improve health care with AI.

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23

Goni, Ibrahim, Asabe S. Ahmadu, and Yusuf M. Malgwi. "Deep Learning in Satellite Imaging: A Survey." Sumerianz Journal of Scientific Research, no. 42 (June 16, 2021): 45–51. http://dx.doi.org/10.47752/sjsr.42.45.51.

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In recent time deep learning has been extensively applied in satellite image analysis, the aim of this work was to conduct a thorough review on the application of deep learning in satellite imaging, moreover we have also provide a detail description regarding the principles of satellite image capturing, in addition to the mathematical models of image processing techniques used in satellite images such as image denoising, image filtering, image segmentation and histogram equalization. We have also discuss some of the aspect of deep learning but not in deep. Finally we have pave away for further research directions both in satellite imaging and deep learning.

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24

Gros, Charley, Andreanne Lemay, Olivier Vincent, Lucas Rouhier, Marie-Helene Bourget, Anthime Bucquet, Joseph Cohen, and Julien Cohen-Adad. "ivadomed: A Medical Imaging Deep Learning Toolbox." Journal of Open Source Software 6, no. 58 (February 12, 2021): 2868. http://dx.doi.org/10.21105/joss.02868.

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25

Lee, June-Goo, Sanghoon Jun, Young-Won Cho, Hyunna Lee, Guk Bae Kim, Joon Beom Seo, and Namkug Kim. "Deep Learning in Medical Imaging: General Overview." Korean Journal of Radiology 18, no. 4 (2017): 570. http://dx.doi.org/10.3348/kjr.2017.18.4.570.

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26

Kim, Jonghoon, Jisu Hong, and Hyunjin Park. "Prospects of deep learning for medical imaging." Precision and Future Medicine 2, no. 2 (June 30, 2018): 37–52. http://dx.doi.org/10.23838/pfm.2018.00030.

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27

Wang Fei, 王飞, 王昊 Wang Hao, 卞耀明 Bian Yaoming, and 司徒国海 Situ Guohai. "Applications of Deep Learning in Computational Imaging." Acta Optica Sinica 40, no. 1 (2020): 0111002. http://dx.doi.org/10.3788/aos202040.0111002.

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28

Zhu, Yong-Le, Rong-Bin She, Wen-Quan Liu, Yuan-Fu Lu, and Guang-Yuan Li. "Deep Learning Optimized Terahertz Single-Pixel Imaging." IEEE Transactions on Terahertz Science and Technology 12, no. 2 (March 2022): 165–72. http://dx.doi.org/10.1109/tthz.2021.3132160.

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29

Ke, Ziwen, Zhuo-Xu Cui, Wenqi Huang, Jing Cheng, Sen Jia, Leslie Ying, Yanjie Zhu, and Dong Liang. "Deep Manifold Learning for Dynamic MR Imaging." IEEE Transactions on Computational Imaging 7 (2021): 1314–27. http://dx.doi.org/10.1109/tci.2021.3131564.

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30

Guo, Zhen, Abraham Levitan, George Barbastathis, and Riccardo Comin. "Randomized probe imaging through deep k-learning." Optics Express 30, no. 2 (January 10, 2022): 2247. http://dx.doi.org/10.1364/oe.445498.

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31

Shan, Caifeng, Tao Tan, Shandong Wu, and Julia A. Schnabel. "Guest Editorial: Deep Learning in Ultrasound Imaging." IEEE Journal of Biomedical and Health Informatics 24, no. 4 (April 2020): 929–30. http://dx.doi.org/10.1109/jbhi.2020.2975858.

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32

Meng, Zhang, Liqi Ding, Shaotong Feng, FangJian Xing, Shouping Nie, Jun Ma, Giancarlo Pedrini, and Caojin Yuan. "Numerical dark-field imaging using deep-learning." Optics Express 28, no. 23 (October 28, 2020): 34266. http://dx.doi.org/10.1364/oe.401786.

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33

Cong, Wenxiang, Yan Xi, Paul Fitzgerald, Bruno De Man, and Ge Wang. "Virtual Monoenergetic CT Imaging via Deep Learning." Patterns 1, no. 8 (November 2020): 100128. http://dx.doi.org/10.1016/j.patter.2020.100128.

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34

Furlow, Bryant. "Deep learning poised to revolutionise diagnostic imaging." Lancet Respiratory Medicine 5, no. 10 (October 2017): 779. http://dx.doi.org/10.1016/s2213-2600(17)30292-8.

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35

Zhai, Xiang, Zheng-dong Cheng, Yang-di Hu, Yi Chen, Zhen-yu Liang, and Yuan Wei. "Foveated ghost imaging based on deep learning." Optics Communications 448 (October 2019): 69–75. http://dx.doi.org/10.1016/j.optcom.2019.05.019.

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36

Yang, Changchun, Hengrong Lan, Feng Gao, and Fei Gao. "Review of deep learning for photoacoustic imaging." Photoacoustics 21 (March 2021): 100215. http://dx.doi.org/10.1016/j.pacs.2020.100215.

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37

Suzuki, Kenji. "Overview of deep learning in medical imaging." Radiological Physics and Technology 10, no. 3 (July 8, 2017): 257–73. http://dx.doi.org/10.1007/s12194-017-0406-5.

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38

Lakhani, Paras, Daniel L. Gray, Carl R. Pett, Paul Nagy, and George Shih. "Hello World Deep Learning in Medical Imaging." Journal of Digital Imaging 31, no. 3 (May 3, 2018): 283–89. http://dx.doi.org/10.1007/s10278-018-0079-6.

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39

Simionescu, Cristian, and Adrian Iftene. "Deep Learning Research Directions in Medical Imaging." Mathematics 10, no. 23 (November 27, 2022): 4472. http://dx.doi.org/10.3390/math10234472.

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In recent years, deep learning has been successfully applied to medical image analysis and provided assistance to medical professionals. Machine learning is being used to offer diagnosis suggestions, identify regions of interest in images, or augment data to remove noise. Training models for such tasks require a large amount of labeled data. It is often difficult to procure such data due to the fact that these requires experts to manually label them, in addition to the privacy and legal concerns that limiting their collection. Due to this, creating self-supervision learning methods and domain-adaptation techniques dedicated to this domain is essential. This paper reviews concepts from the field of deep learning and how they have been applied to medical image analysis. We also review the current state of self-supervised learning methods and their applications to medical images. In doing so, we will also present the resource ecosystem of researchers in this field, such as datasets, evaluation methodologies, and benchmarks.

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40

Wang, Lulu. "Deep Learning Techniques to Diagnose Lung Cancer." Cancers 14, no. 22 (November 13, 2022): 5569. http://dx.doi.org/10.3390/cancers14225569.

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Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. This paper presents the recent development of deep learning-based imaging techniques for early lung cancer detection.

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41

Madhumathy, P., and Digvijay Pandey. "Deep learning based photo acoustic imaging for non-invasive imaging." Multimedia Tools and Applications 81, no. 5 (January 28, 2022): 7501–18. http://dx.doi.org/10.1007/s11042-022-11903-6.

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42

Ma, Zhuoran, Feifei Wang, Weizhi Wang, Yeteng Zhong, and Hongjie Dai. "Deep learning for in vivo near-infrared imaging." Proceedings of the National Academy of Sciences 118, no. 1 (December 28, 2020): e2021446118. http://dx.doi.org/10.1073/pnas.2021446118.

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Detecting fluorescence in the second near-infrared window (NIR-II) up to ∼1,700 nm has emerged as a novel in vivo imaging modality with high spatial and temporal resolution through millimeter tissue depths. Imaging in the NIR-IIb window (1,500–1,700 nm) is the most effective one-photon approach to suppressing light scattering and maximizing imaging penetration depth, but relies on nanoparticle probes such as PbS/CdS containing toxic elements. On the other hand, imaging the NIR-I (700–1,000 nm) or NIR-IIa window (1,000–1,300 nm) can be done using biocompatible small-molecule fluorescent probes including US Food and Drug Administration-approved dyes such as indocyanine green (ICG), but has a caveat of suboptimal imaging quality due to light scattering. It is highly desired to achieve the performance of NIR-IIb imaging using molecular probes approved for human use. Here, we trained artificial neural networks to transform a fluorescence image in the shorter-wavelength NIR window of 900–1,300 nm (NIR-I/IIa) to an image resembling an NIR-IIb image. With deep-learning translation, in vivo lymph node imaging with ICG achieved an unprecedented signal-to-background ratio of >100. Using preclinical fluorophores such as IRDye-800, translation of ∼900-nm NIR molecular imaging of PD-L1 or EGFR greatly enhanced tumor-to-normal tissue ratio up to ∼20 from ∼5 and improved tumor margin localization. Further, deep learning greatly improved in vivo noninvasive NIR-II light-sheet microscopy (LSM) in resolution and signal/background. NIR imaging equipped with deep learning could facilitate basic biomedical research and empower clinical diagnostics and imaging-guided surgery in the clinic.

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43

Khosravi, Bardia, Pouria Rouzrokh, Shahriar Faghani, Mana Moassefi, Sanaz Vahdati, Elham Mahmoudi, Hamid Chalian, and Bradley J. Erickson. "Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review." Diagnostics 12, no. 10 (October 17, 2022): 2512. http://dx.doi.org/10.3390/diagnostics12102512.

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Machine-learning (ML) and deep-learning (DL) algorithms are part of a group of modeling algorithms that grasp the hidden patterns in data based on a training process, enabling them to extract complex information from the input data. In the past decade, these algorithms have been increasingly used for image processing, specifically in the medical domain. Cardiothoracic imaging is one of the early adopters of ML/DL research, and the COVID-19 pandemic resulted in more research focus on the feasibility and applications of ML/DL in cardiothoracic imaging. In this scoping review, we systematically searched available peer-reviewed medical literature on cardiothoracic imaging and quantitatively extracted key data elements in order to get a big picture of how ML/DL have been used in the rapidly evolving cardiothoracic imaging field. During this report, we provide insights on different applications of ML/DL and some nuances pertaining to this specific field of research. Finally, we provide general suggestions on how researchers can make their research more than just a proof-of-concept and move toward clinical adoption.

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44

Li, Fengrong, Yifan Sun, and XiangDong Zhang. "Deep-learning-based quantum imaging using NOON states." Journal of Physics Communications 6, no. 3 (March 1, 2022): 035005. http://dx.doi.org/10.1088/2399-6528/ac5e25.

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Abstract The phase sensitivity of photonic NOON states scales O(1/N), which reaches the Heisenberg limit and indicates a great potential in high-quality optical phase sensing. However, the NOON states with large photon number N are experimentally difficult both to prepare and to operate. Such a fact severely limits their practical use. In this article, we soften the requirements for high-quality imaging based on NOON states with large N by introducing deep-learning methods. Specifically, we show that, with the help of deep-learning network, the fluctuation of the images obtained by the NOON states when N = 2 can be reduced to that of the currently infeasible imaging by the NOON states when N = 8. We numerically investigate our results obtained by two types of deep-learning models—deep neural network and convolutional denoising autoencoders, and characterize the imaging quality using the root mean square error. By comparison, we find that small-N NOON state imaging data is sufficient for training the deep-learning models of our schemes, which supports its direct application to the imaging processes.

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45

Zhang, Yudong, Juan Manuel Gorriz, and Zhengchao Dong. "Deep Learning in Medical Image Analysis." Journal of Imaging 7, no. 4 (April 20, 2021): 74. http://dx.doi.org/10.3390/jimaging7040074.

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46

Czum, Julianna M. "Dive Into Deep Learning." Journal of the American College of Radiology 17, no. 5 (May 2020): 637–38. http://dx.doi.org/10.1016/j.jacr.2020.02.005.

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47

McBee, Morgan P., Omer A. Awan, Andrew T. Colucci, Comeron W. Ghobadi, Nadja Kadom, Akash P. Kansagra, Srini Tridandapani, and William F. Auffermann. "Deep Learning in Radiology." Academic Radiology 25, no. 11 (November 2018): 1472–80. http://dx.doi.org/10.1016/j.acra.2018.02.018.

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48

Sanders, Jeremiah W., Justin R. Fletcher, Steven J. Frank, Ho-Ling Liu, Jason M. Johnson, Zijian Zhou, Henry Szu-Meng Chen, et al. "Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging." SoftwareX 10 (July 2019): 100347. http://dx.doi.org/10.1016/j.softx.2019.100347.

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49

Ma, Jiechao, Yang Song, Xi Tian, Yiting Hua, Rongguo Zhang, and Jianlin Wu. "Survey on deep learning for pulmonary medical imaging." Frontiers of Medicine 14, no. 4 (December 16, 2019): 450–69. http://dx.doi.org/10.1007/s11684-019-0726-4.

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AbstractAs a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

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50

Ostvik, Andreas, Ivar Mjaland Salte, Erik Smistad, Thuy Mi Nguyen, Daniela Melichova, Harald Brunvand, Kristina Haugaa, Thor Edvardsen, Bjornar Grenne, and Lasse Lovstakken. "Myocardial Function Imaging in Echocardiography Using Deep Learning." IEEE Transactions on Medical Imaging 40, no. 5 (May 2021): 1340–51. http://dx.doi.org/10.1109/tmi.2021.3054566.

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