Relevant bibliographies by topics / Deep Learning Imaging

Academic literature on the topic 'Deep Learning Imaging'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

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"

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Deep Learning Imaging"

1

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 text
Abstract:
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: 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
APA, Harvard, Vancouver, ISO, and other styles
2

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 text
Abstract:
This thesis investigated novel deep learning techniques for advanced medical imaging applications. It addressed three major research issues of employing deep learning for medical imaging applications including network architecture, lack of training data, and generalisation. It proposed three new frameworks for CNN network architecture and three novel transfer learning methods. The proposed solutions have been tested on four different medical imaging applications demonstrating their effectiveness and generalisation. These solutions have already been employed by the scientific community showing excellent performance in medical imaging applications and other domains.
APA, Harvard, Vancouver, ISO, and other styles
3

Bernal, 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 text
Abstract:
The quantification of cerebral atrophy is fundamental in neuroinformatics since it permits diagnosing brain diseases, assessing their progression, and determining the effectiveness of novel treatments to counteract them. However, this is still an open and challenging problem since the performance 2/2 of traditional methods depends on imaging protocols and quality, data harmonisation errors, and brain abnormalities. In this doctoral thesis, we question whether deep learning methods can be used for better estimating cerebral atrophy from magnetic resonance images. Our work shows that deep learning can lead to a state-of-the-art performance in cross-sectional assessments and compete and surpass traditional longitudinal atrophy quantification methods. We believe that the proposed cross-sectional and longitudinal methods can be beneficial for the research and clinical community
La 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
APA, Harvard, Vancouver, ISO, and other styles
4

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 text
Abstract:
Recent improvements in deep learning architectures, combined with the strength of modern computing hardware such as graphics processing units, has lead to significant results in the field of image analysis. In this thesis work, locally connected architectures are employed to reduce noise in flash X-ray diffraction images. The layers in these architectures use convolutional kernels, but without shared weights. This combines the benefits of lower model memory footprint in convolutional networks with the higher model capacity of fully connected networks. Since the camera used to capture the diffraction images has pixelwise unique characteristics, and thus lacks equivariance, this compromise can be beneficial. The background images of this thesis work were generated with an active laser but without injected samples. Artificial diffraction patterns were then added to these background images allowing for training U-Net architectures to separate them. Architecture A achieved a performance of 0.187 on the test set, roughly translating to 35 fewer photon errors than a model similar to state of the art. After smoothing the photon errors this performance increased to 0.285, since the U-Net architectures managed to remove flares where state of the art could not. This could be taken as a proof of concept that locally connected networks are able to separate diffraction from background in flash X-Ray imaging.
APA, Harvard, Vancouver, ISO, and other styles
5

Forsgren, 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 text
APA, Harvard, Vancouver, ISO, and other styles
6

McCamey, 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 text
APA, Harvard, Vancouver, ISO, and other styles
7

Wajngot, 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 text
Abstract:
Cardiovascular diseases are the largest mortality factor globally, and early diagnosis is essential for a proper medical response. Cardiac computed tomography can be used to acquire images for their diagnosis, but without radiation dose reduction the radiation emitted to the patient becomes a significant risk factor. By reducing the dose, the image quality is often compromised, and determining a diagnosis becomes difficult. This project proposes image quality enhancement with deep learning. A cycle-consistent generative adversarial neural network was fed low- and high-quality images with the purpose to learn to translate between them. By using a cycle-consistency cost it was possible to train the network without paired data. With this method, a low-quality image acquired from a computed tomography scan with dose reduction could be enhanced in post processing. The results were mixed but showed an increase of ventricular contrast and artifact mitigation. The technique comes with several problems that are yet to be solved, such as structure alterations, but it shows promise for continued development.
APA, Harvard, Vancouver, ISO, and other styles
8

Nie, 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 text
Abstract:
Melanoma is one of the deadliest forms of cancer. Unfortunately, its incidence rates have been increasing all over the world. One of the techniques used by dermatologists to diagnose melanomas is an imaging modality called dermoscopy. The skin lesion is inspected using a magnification device and a light source. This technique makes it possible for the dermatologist to observe subcutaneous structures that would be invisible otherwise. However, the use of dermoscopy is not straightforward, requiring years of practice. Moreover, the diagnosis is many times subjective and challenging to reproduce. Therefore, it is necessary to develop automatic methods that will help dermatologists provide more reliable diagnoses.  Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. Recent developments have converged to make fully automatic early melanoma detection a real possibility. First, the advent of dermoscopy has enabled a dramatic boost in the clinical diagnostic ability to the point that it can detect melanoma in the clinic at the earliest stages. This technology’s global adoption has allowed the accumulation of extensive collections of dermoscopy images. The development of advanced technologies in image processing and machine learning has given us the ability to distinguish malignant melanoma from the many benign mimics that require no biopsy. These new technologies should allow earlier detection of melanoma and reduce a large number of unnecessary and costly biopsy procedures. Although some of the new systems reported for these technologies have shown promise in preliminary trials, a widespread implementation must await further technical progress in accuracy and reproducibility.  This thesis provides an overview of our deep learning (DL) based methods used in the diagnosis of melanoma in dermoscopy images. First, we introduce the background. Then, this paper gives a brief overview of the state-of-art article on melanoma interpret. After that, a review is provided on the deep learning models for melanoma image analysis and the main popular techniques to improve the diagnose performance. We also made a summary of our research results. Finally, we discuss the challenges and opportunities for automating melanocytic skin lesions’ diagnostic procedures. We end with an overview of a conclusion and directions for the following research plan.
APA, Harvard, Vancouver, ISO, and other styles
9

Hoffmire, 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 text
APA, Harvard, Vancouver, ISO, and other styles
10

Marini, Michela. "Representation learning and applications in neuronal imaging." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19776/.

Full text
Abstract:
Confocal fluorescence microscopy is a microscopic technique that provides true three-dimensional (3D) optical resolution and that allows the visualization of molecular expression patterns and morphological structures. This technique has therefore become increasingly more important in neuroscience, due to its applications in image-based screening and profiling of neurons. However, in the last two decades, many approaches have been introduced to segment the neurons automatically. With the more recent advances in the field of neural networks and Deep Learning, multiple methods have been implemented with focus on the segmentation and delineation of the neuronal trees and somas. Deep Learning methods, such as the Convolutional Neural Networks (CNN), have recently become one of the new trends in the Computer Vision area. Their ability to find strong spatially local correlations in the data at different abstraction levels allows them to learn a set of filters that are useful to correctly segment the data, when given a labeled training set. The overall aim of this thesis was to develop a new algorithm for automated segmentation of confocal neuronal images based on Deep Learning techniques. In order to realize this goal, we implemented a U-Net-based CNN and realized the dataset necessary to train the Neural Network. The results show how satisfactory segmentations are achieved for all the test images given in input to our algorithm, by obtaining a Dice coefficient, as average of all the images of the test dataset, greater than 0.9.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Deep Learning Imaging"

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Deep Learning Imaging"

1

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 text
APA, Harvard, Vancouver, ISO, and other styles
2

Sarkar, 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 text
APA, Harvard, Vancouver, ISO, and other styles
3

Bourdon, 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 text
APA, Harvard, Vancouver, ISO, and other styles
4

Hatamizadeh, 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 text
APA, Harvard, Vancouver, ISO, and other styles
5

Ruizhongtai 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 text
APA, Harvard, Vancouver, ISO, and other styles
6

Tetteh, 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 text
APA, Harvard, Vancouver, ISO, and other styles
7

Manjón, 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 text
APA, Harvard, Vancouver, ISO, and other styles
8

Maitra, 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 text
APA, Harvard, Vancouver, ISO, and other styles
9

Saraf, 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 text
APA, Harvard, Vancouver, ISO, and other styles
10

Leibowitz, 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 text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Deep Learning Imaging"

1

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 text
APA, Harvard, Vancouver, ISO, and other styles
2

Di, 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 text
APA, Harvard, Vancouver, ISO, and other styles
3

Tahmassebi, 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 text
APA, Harvard, Vancouver, ISO, and other styles
4

Barbastathis, 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 text
APA, Harvard, Vancouver, ISO, and other styles
5

Jayan, 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 text
APA, Harvard, Vancouver, ISO, and other styles
6

He, 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 text
APA, Harvard, Vancouver, ISO, and other styles
7

Paul, 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 text
APA, Harvard, Vancouver, ISO, and other styles
8

Chudzik, 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 text
APA, Harvard, Vancouver, ISO, and other styles
9

Wang, 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 text
APA, Harvard, Vancouver, ISO, and other styles
10

Liu, 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 text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Deep Learning Imaging"

1

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
Abstract:
Riprap rock and aggregates are extensively used in structural, transportation, geotechnical, and hydraulic engineering applications. Field determination of morphological properties of aggregates such as size and shape can greatly facilitate the quality assurance/quality control (QA/QC) process for proper aggregate material selection and engineering use. Many aggregate imaging approaches have been developed to characterize the size and morphology of individual aggregates by computer vision. However, 3D field characterization of aggregate particle morphology is challenging both during the quarry production process and at construction sites, particularly for aggregates in stockpile form. This research study presents a 3D reconstruction-segmentation-completion approach based on deep learning techniques by combining three developed research components: field 3D reconstruction procedures, 3D stockpile instance segmentation, and 3D shape completion. The approach was designed to reconstruct aggregate stockpiles from multi-view images, segment the stockpile into individual instances, and predict the unseen side of each instance (particle) based on the partial visible shapes. Based on the dataset constructed from individual aggregate models, a state-of-the-art 3D instance segmentation network and a 3D shape completion network were implemented and trained, respectively. The application of the integrated approach was demonstrated on re-engineered stockpiles and field stockpiles. The validation of results using ground-truth measurements showed satisfactory algorithm performance in capturing and predicting the unseen sides of aggregates. The algorithms are integrated into a software application with a user-friendly graphical user interface. Based on the findings of this study, this stockpile aggregate analysis approach is envisioned to provide efficient field evaluation of aggregate stockpiles by offering convenient and reliable solutions for on-site QA/QC tasks of riprap rock and aggregate stockpiles.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Deep Learning Imaging' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography