Academic literature on the topic 'Deep Learning Imaging'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Deep Learning Imaging.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Deep Learning Imaging"
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.
Full textWang, 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.
Full textSengupta, 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.
Full textStrack, Rita. "Deep learning in imaging." Nature Methods 16, no. 1 (December 20, 2018): 17. http://dx.doi.org/10.1038/s41592-018-0267-9.
Full textKlang, 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.
Full textvan 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.
Full textKim, 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.
Full textKim, 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.
Full textLiu, 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.
Full textCurrie, 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.
Full textDissertations / Theses on the topic "Deep Learning Imaging"
Li, Shuai Ph D. Massachusetts Institute of Technology. "Computational imaging through deep learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122070.
Full textThesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 143-154).
Computational imaging (CI) is a class of imaging systems that uses inverse algorithms to recover an unknown object from the physical measurement. Traditional inverse algorithms in CI obtain an estimate of the object by minimizing the Tikhonov functional, which requires explicit formulations of the forward operator of the physical system, as well as the prior knowledge about the class of objects being imaged. In recent years, machine learning architectures, and deep learning (DL) in particular, have attracted increasing attentions from CI researchers. Unlike traditional inverse algorithms in CI, DL approach learns both the forward operator and the objects' prior implicitly from training examples. Therefore, it is especially attractive when the forward imaging model is uncertain (e.g. imaging through random scattering media), or the prior about the class of objects is difficult to be expressed analytically (e.g. natural images).
In this thesis, the application of DL approaches in two different CI scenarios are investigated: imaging through a glass diffuser and quantitative phase retrieval (QPR), where an Imaging through Diffuser Network (IDiffNet) and a Phase Extraction Neural Network (PhENN) are experimentally demonstrated, respectively. This thesis also studies the influences of the two main factors that determine the performance of a trained neural network: network architecture (connectivity, network depth, etc) and training example quality (spatial frequency content in particular). Motivated by the analysis of the latter factor, two novel approaches, spectral pre-modulation approach and Learning Synthesis by DNN (LS-DNN) method, are successively proposed to improve the visual qualities of the network outputs. Finally, the LS-DNN enhanced PhENN is applied to a phase microscope to recover the phase of a red blood cell (RBC) sample.
Furthermore, through simulation of the learned weak object transfer function (WOTF) and experiment on a star-like phase target, we demonstrate that our network has indeed learned the correct physical model rather than doing something trivial as pattern matching.
by Shuai Li.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Mechanical Engineering
Alzubaidi, Laith. "Deep learning for medical imaging applications." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/227812/1/Laith_Alzubaidi_Thesis.pdf.
Full textBernal, Moyano Jose. "Deep learning for atrophy quantification in brain magnetic resonance imaging." Doctoral thesis, Universitat de Girona, 2020. http://hdl.handle.net/10803/671699.
Full textLa cuantificación de la atrofia cerebral es fundamental en la neuroinformática ya que permite diagnosticar enfermedades cerebrales, evaluar su progresión y determinar la eficacia de los nuevos tratamientos para contrarrestarlas. Sin embargo, éste sigue siendo un problema abierto y difícil, ya que el rendimiento de los métodos tradicionales depende de los protocolos y la calidad de las imágenes, los errores de armonización de los datos y las anomalías del cerebro. En esta tesis doctoral, cuestionamos si los métodos de aprendizaje profundo pueden ser utilizados para estimar mejor la atrofia cerebral a partir de imágenes de resonancia magnética. Nuestro trabajo muestra que el aprendizaje profundo puede conducir a un rendimiento de vanguardia en las evaluaciones transversales y competir y superar los métodos tradicionales de cuantificación de la atrofia longitudinal. Creemos que los métodos transversales y longitudinales propuestos pueden ser beneficiosos para la comunidad investigadora y clínica
Sundman, Tobias. "Noise Reduction in Flash X-ray Imaging Using Deep Learning." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-355731.
Full textForsgren, Edvin. "Deep Learning to Enhance Fluorescent Signals in Live Cell Imaging." Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-175328.
Full textMcCamey, Morgan R. "Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy." Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1624266549100904.
Full textWajngot, David. "Improving Image Quality in Cardiac Computed Tomography using Deep Learning." Thesis, Linköpings universitet, Avdelningen för kardiovaskulär medicin, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-154506.
Full textNie, Yali. "Automatic Melanoma Diagnosis in Dermoscopic Imaging Base on Deep Learning System." Licentiate thesis, Mittuniversitetet, Institutionen för elektronikkonstruktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-41751.
Full textHoffmire, Matthew A. "Deep Learning for Anisoplanatic Optical Turbulence Mitigation in Long Range Imaging." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1607694391536891.
Full textMarini, Michela. "Representation learning and applications in neuronal imaging." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19776/.
Full textBooks on the topic "Deep Learning Imaging"
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.
Full textJain, 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.
Full textJain, 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.
Full textLu, 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.
Full textSURI, Biswas. Multimodality Imaging: Deep Learning A. Institute of Physics Publishing, 2022.
Find full textPaul, Sudip, and Sanjay Saxena. Deep Learning Applications in Medical Imaging. IGI Global, 2020.
Find full textPaul, Sudip, and Sanjay Saxena. Deep Learning Applications in Medical Imaging. IGI Global, 2020.
Find full textDeep Learning Models for Medical Imaging. Elsevier, 2022. http://dx.doi.org/10.1016/c2020-0-00344-0.
Full textDas, Nibaran, K. C. Santosh, and Swarnendu Ghosh. Deep Learning Models for Medical Imaging. Elsevier Science & Technology Books, 2021.
Find full textPaul, Sudip, and Sanjay Saxena. Deep Learning Applications in Medical Imaging. IGI Global, 2020.
Find full textBook chapters on the topic "Deep Learning Imaging"
Runge, Val M., and Johannes T. Heverhagen. "Deep Learning: For Imaging Reconstruction." In The Physics of Clinical MR Taught Through Images, 338. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85413-3_150.
Full textSarkar, Arjun. "Deep Learning in Medical Imaging." In Knowledge Modelling and Big Data Analytics in Healthcare, 107–32. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003142751-8.
Full textBourdon, Pascal, Olfa Ben Ahmed, Thierry Urruty, Khalifa Djemal, and Christine Fernandez-Maloigne. "Explainable AI for Medical Imaging: Knowledge Matters." In Multi-faceted Deep Learning, 267–92. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-030-74478-6_11.
Full textHatamizadeh, Ali, Assaf Hoogi, Debleena Sengupta, Wuyue Lu, Brian Wilcox, Daniel Rubin, and Demetri Terzopoulos. "Deep Active Lesion Segmentation." In Machine Learning in Medical Imaging, 98–105. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32692-0_12.
Full textRuizhongtai Qi, Charles. "Deep Learning on 3D Data." In 3D Imaging, Analysis and Applications, 513–66. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44070-1_11.
Full textTetteh, Giles, Markus Rempfler, Claus Zimmer, and Bjoern H. Menze. "Deep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction." In Machine Learning in Medical Imaging, 344–52. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67389-9_40.
Full textManjón, José V., and Pierrick Coupe. "MRI Denoising Using Deep Learning." In Patch-Based Techniques in Medical Imaging, 12–19. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00500-9_2.
Full textMaitra, Sanjit, Ratul Ghosh, and Kuntal Ghosh. "Applications of Deep Learning in Medical Imaging." In Handbook of Deep Learning Applications, 111–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11479-4_6.
Full textSaraf, Vaibhav, Pallavi Chavan, and Ashish Jadhav. "Deep Learning Challenges in Medical Imaging." In Algorithms for Intelligent Systems, 293–301. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3242-9_28.
Full textLeibowitz, Carla. "Arterys: Deep Learning for Medical Imaging." In Demystifying Big Data and Machine Learning for Healthcare, 169–73. Boca Raton : Taylor & Francis, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315389325-16.
Full textConference papers on the topic "Deep Learning Imaging"
Nguyen, Thanh C., George Nehmetallah, and Lei Tian. "Deep learning in computational microscopy." In Computational Imaging IV, edited by Jonathan C. Petruccelli, Abhijit Mahalanobis, and Lei Tian. SPIE, 2019. http://dx.doi.org/10.1117/12.2520089.
Full textDi, Jianglei, Kaiqiang Wang, and Jianlin Zhao. "Deep learning in computational imaging." In Holography, Diffractive Optics, and Applications X, edited by Changhe Zhou, Yunlong Sheng, and Liangcai Cao. SPIE, 2020. http://dx.doi.org/10.1117/12.2573707.
Full textTahmassebi, Amirhessam, Amir H. Gandomi, Ian McCann, Mieke H. J. Schulte, Anna E. Goudriaan, and Anke Meyer-Baese. "Deep Learning in Medical Imaging." In PEARC '18: Practice and Experience in Advanced Research Computing. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3219104.3229250.
Full textBarbastathis, George, Alexandre Goy, Kwabena Arthur, Mo Deng, and Shuai Li. "Computational imaging via deep learning." In Integrated Photonics Research, Silicon and Nanophotonics. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/iprsn.2018.ith4b.1.
Full textJayan, Athulya, Fathima L, Nazarulla N, Sangeeth S, and Dhanya M. "Medical Imaging Using Deep Learning." In 2022 International Conference on Innovations in Science and Technology for Sustainable Development (ICISTSD). IEEE, 2022. http://dx.doi.org/10.1109/icistsd55159.2022.10010394.
Full textHe, Ji, and Jianhua Ma. "Radon inversion via deep learning." In Physics of Medical Imaging, edited by Hilde Bosmans, Guang-Hong Chen, and Taly Gilat Schmidt. SPIE, 2019. http://dx.doi.org/10.1117/12.2511643.
Full textPaul, Justin S., Andrew J. Plassard, Bennett A. Landman, and Daniel Fabbri. "Deep learning for brain tumor classification." In SPIE Medical Imaging, edited by Andrzej Krol and Barjor Gimi. SPIE, 2017. http://dx.doi.org/10.1117/12.2254195.
Full textChudzik, Piotr, Bashir Al-Diri, Francesco Caliva, Giovanni Ometto, and Andrew Hunter. "Learning deep similarity in fundus photography." In SPIE Medical Imaging, edited by Martin A. Styner and Elsa D. Angelini. SPIE, 2017. http://dx.doi.org/10.1117/12.2254286.
Full textWang, Hongda, Yair Rivenson, Hatice C. Koydemir, Zhensong Wei, Zhengshuang Ren, Harun Gunaydin, Yibo Zhang, et al. "Deep Learning Enhances Mobile Microscopy." In Computational Optical Sensing and Imaging. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/cosi.2018.cth1b.2.
Full textLiu, Tairan, Kevin De Haan, Bijie Bai, Yair Rivenson, Yi Luo, Hongda Wang, David Karalli, et al. "Holographic polarization microscopy using deep learning." In Quantitative Phase Imaging VII, edited by Gabriel Popescu, YongKeun Park, and Yang Liu. SPIE, 2021. http://dx.doi.org/10.1117/12.2580286.
Full textReports on the topic "Deep Learning Imaging"
Huang, Haohang, Erol Tutumluer, Jiayi Luo, Kelin Ding, Issam Qamhia, and John Hart. 3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates—Phase 2. Illinois Center for Transportation, September 2022. http://dx.doi.org/10.36501/0197-9191/22-017.
Full text