Добірка наукової літератури з теми "Deep Learning Imaging"
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Статті в журналах з теми "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.
Повний текст джерелаWang, 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.
Повний текст джерелаSengupta, 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.
Повний текст джерелаStrack, Rita. "Deep learning in imaging." Nature Methods 16, no. 1 (December 20, 2018): 17. http://dx.doi.org/10.1038/s41592-018-0267-9.
Повний текст джерелаKlang, 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.
Повний текст джерелаvan 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.
Повний текст джерелаKim, 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.
Повний текст джерелаKim, 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.
Повний текст джерелаLiu, 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.
Повний текст джерелаCurrie, 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.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаThesis: 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.
Повний текст джерелаBernal, Moyano Jose. "Deep learning for atrophy quantification in brain magnetic resonance imaging." Doctoral thesis, Universitat de Girona, 2020. http://hdl.handle.net/10803/671699.
Повний текст джерелаLa 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.
Повний текст джерелаForsgren, 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.
Повний текст джерелаMcCamey, 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.
Повний текст джерелаWajngot, 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.
Повний текст джерелаNie, 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.
Повний текст джерелаHoffmire, 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.
Повний текст джерелаMarini, Michela. "Representation learning and applications in neuronal imaging." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19776/.
Повний текст джерелаКниги з теми "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.
Повний текст джерелаJain, 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.
Повний текст джерелаJain, 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.
Повний текст джерелаLu, 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.
Повний текст джерелаSURI, Biswas. Multimodality Imaging: Deep Learning A. Institute of Physics Publishing, 2022.
Знайти повний текст джерелаPaul, Sudip, and Sanjay Saxena. Deep Learning Applications in Medical Imaging. IGI Global, 2020.
Знайти повний текст джерелаPaul, Sudip, and Sanjay Saxena. Deep Learning Applications in Medical Imaging. IGI Global, 2020.
Знайти повний текст джерелаDeep Learning Models for Medical Imaging. Elsevier, 2022. http://dx.doi.org/10.1016/c2020-0-00344-0.
Повний текст джерелаDas, Nibaran, K. C. Santosh, and Swarnendu Ghosh. Deep Learning Models for Medical Imaging. Elsevier Science & Technology Books, 2021.
Знайти повний текст джерелаPaul, Sudip, and Sanjay Saxena. Deep Learning Applications in Medical Imaging. IGI Global, 2020.
Знайти повний текст джерелаЧастини книг з теми "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.
Повний текст джерелаSarkar, 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.
Повний текст джерелаBourdon, 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.
Повний текст джерелаHatamizadeh, 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.
Повний текст джерелаRuizhongtai 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.
Повний текст джерелаTetteh, 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.
Повний текст джерелаManjón, 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.
Повний текст джерелаMaitra, 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.
Повний текст джерелаSaraf, 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.
Повний текст джерелаLeibowitz, 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.
Повний текст джерелаТези доповідей конференцій з теми "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.
Повний текст джерелаDi, 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.
Повний текст джерелаTahmassebi, 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.
Повний текст джерелаBarbastathis, 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.
Повний текст джерелаJayan, 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.
Повний текст джерелаHe, 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.
Повний текст джерелаPaul, 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.
Повний текст джерелаChudzik, 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.
Повний текст джерелаWang, 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.
Повний текст джерелаLiu, 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.
Повний текст джерелаЗвіти організацій з теми "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.
Повний текст джерела