Internet Archive Scholar
Filters
























64 Hits in 9.0 sec

Three-dimensional radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture

Dan Nguyen, Xun Jia, David Sher, Mu-Han Lin, Zohaib Iqbal, Hui Liu, Steve B Jiang
2019 Physics in Medicine and Biology  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (2.0 MB)
https://web.archive.org/web/20200928133707/https://arxiv.org/vc/arxiv/papers/1805/1805.10397v2.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL. The file type is application/pdf.

Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:

2018 pre-print arXiv:1805.10397v2 fatcat:vl4v2tm45zbktcg65iue7e6stq

Other Versions

2019 accepted
arXiv:1805.10397v3 fatcat:ckfcovjcsvhildhhszhll4oaum
2018 pre-print
arXiv:1805.10397v1 fatcat:uxdfmoxcpngmhdc2gxbhqzb3gu
To solve this problem, we propose to investigate a deep learning-based dose prediction model, Hierarchically Densely Connected U-net, based on two highly popular network architectures: U-net and DenseNet  ...  The treatment planning process for patients with head and neck (H&N) cancer is regarded as one of the most complicated due to large target volume, multiple prescription dose levels, and many radiation-sensitive  ...  CONCLUSION We have developed and proposed a hierarchically densely connected U-net architecture, HD U-net, and applied the model to volumetric dose prediction for patients with H&N cancer.  ... 
doi:10.1088/1361-6560/ab039b pmid:30703760 fatcat:akqxtoiifjafvbch3vhilxg5ny
Multiple Versions
Citation

Dan Nguyen, Xun Jia, David Sher, Mu-Han Lin, Zohaib Iqbal, Hui Liu, Steve B Jiang. "Three-dimensional radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture." Physics in Medicine and Biology 64.6 (2019) 065020

A Feasibility Study on Deep Learning-Based Radiotherapy Dose Calculation [article]

Yixun Xing, Dan Nguyen, Weiguo Lu, Ming Yang, Steve Jiang
2019 arXiv   pre-print

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (1.5 MB)
https://web.archive.org/web/20191222124248/https://arxiv.org/pdf/1908.03159v1.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL. The file type is application/pdf.

Methods: We developed a new radiotherapy dose calculation engine based on a modified Hierarchically Densely Connected U-net (HD U-net) model and tested its feasibility with prostate intensity-modulated  ...  Mapping from an IMRT fluence map domain to a 3D dose domain requires a deep neural network of complicated architecture and a huge training dataset.  ...  Acknowledgements We would like to thank the Cancer Prevention and Research Institute of Texas (CPRIT) for providing support through grants IIRACA RP160190 and IIRA RP150485.  ... 
arXiv:1908.03159v1 fatcat:5osvr6utffhqfo4iokeotg6i2e
Citation

Yixun Xing, Dan Nguyen, Weiguo Lu, Ming Yang, Steve Jiang. "A Feasibility Study on Deep Learning-Based Radiotherapy Dose Calculation." arXiv (2019)

A Review on Application of Deep Learning Algorithms in External Beam Radiotherapy Automated Treatment Planning

Mingqing Wang, Qilin Zhang, Saikit Lam, Jing Cai, Ruijie Yang
2020 Frontiers in Oncology  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (253.4 kB)
https://web.archive.org/web/20201025144612/https://fjfsdata01prod.blob.core.windows.net/articles/files/580919/pubmed-zip/.versions/1/.package-entries/fonc-10-580919/fonc-10-580919.pdf?sv=2018-03-28&sr=b&sig=LWWo%2F7XFrllgc%2FWo574SuVR5dQC6ie8vc0OjQ33UiUE%3D&se=2020-10-25T14%3A46%3A41Z&sp=r&rscd=attachment%3B%20filename%2A%3DUTF-8%27%27fonc-10-580919.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL. The file type is application/pdf.

Artificial intelligence, including machine learning (ML) and deep learning (DL), has been recently used to automate RT treatment planning and has gained enormous attention in the RT community due to its  ...  Treatment planning plays an important role in the process of radiotherapy (RT). The quality of the treatment plan directly and significantly affects patient treatment outcomes.  ...  Nguyen et al. (23) further proposed a hierarchically densely connected U-Net (HD U-Net) for 3D dose distribution prediction for head and neck cancer patients treated with volumetric-modulated arc therapy  ... 
doi:10.3389/fonc.2020.580919 pmid:33194711 pmcid:PMC7645101 fatcat:gbijfefejna2zg2egmqnpq4f4a
DOAJ
Citation

Mingqing Wang, Qilin Zhang, Saikit Lam, Jing Cai, Ruijie Yang. "A Review on Application of Deep Learning Algorithms in External Beam Radiotherapy Automated Treatment Planning." Frontiers in Oncology (2020) 580919

Knowledge-based Radiation Treatment Planning: A Data-driven Method Survey [article]

Shadab Momin, Yabo Fu, Yang Lei, Justin Roper, Jeffrey D. Bradley, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print

Preserved Fulltext

Web Archive Capture PDF (401.8 kB)
https://web.archive.org/web/20200928221043/https://arxiv.org/pdf/2009.07388v2.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL. The file type is application/pdf.

Other Versions

2020 pre-print
arXiv:2009.07388v1 fatcat:mmyxv5lrajfxhonvffyqgy4fwm
in traditional methods category, whereas deep-learning-based methods included studies that trained neural networks to make dose prediction.  ...  A comprehensive review of each category is presented, highlighting key parameters, methods, and their outlooks in terms of dose prediction over the years.  ...  U-Net permits effective feature learning even with small number of training sample size. Milletary et al. proposed a three dimensional variant of U-Net known as V-Net [91] .  ... 
arXiv:2009.07388v2 fatcat:rygjxff535dsnhq5gq3ri3fls4
Multiple Versions
Citation

Shadab Momin, Yabo Fu, Yang Lei, Justin Roper, Jeffrey D. Bradley, Walter J. Curran, Tian Liu, Xiaofeng Yang. "Knowledge-based Radiation Treatment Planning: A Data-driven Method Survey." arXiv (2020)

Deep Learning in Multi-organ Segmentation [article]

Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print

Preserved Fulltext

Web Archive Capture PDF (836.8 kB)
https://web.archive.org/web/20200321091621/https://arxiv.org/pdf/2001.10619v1.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL. The file type is application/pdf.

This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications.  ...  datasets and 2015 MICCAI Head Neck Auto-Segmentation Challenge datasets.  ...  ACKNOWLEDGEMENT This research was supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718 and Emory Winship Cancer Institute pilot grant.  ... 
arXiv:2001.10619v1 fatcat:6uwqwnzydzccblh5cajhsgdpea
Citation

Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu, Xiaofeng Yang. "Deep Learning in Multi-organ Segmentation." arXiv (2020)

Front Matter: Volume 11313

Bennett A. Landman, Ivana Išgum
2020 Medical Imaging 2020: Image Processing  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (979.9 kB)
https://web.archive.org/web/20200507093602/https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11313/1131301/Front-Matter-Volume-11313/10.1117/12.2570657.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL. The file type is application/pdf.

The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee.  ...  using a Base 36 numbering system employing both numerals and letters.  ...  [11313-72] 11313 22 Estimating standard-dose PET from low-dose PET with deep learning [11313-73] 11313 23 Internal-transfer weighting of multi-task learning for lung cancer detection [11313-74]  ... 
doi:10.1117/12.2570657 fatcat:be32besqknaybh6wibz7unuboa
Citation

Bennett A. Landman, Ivana Išgum. "Front Matter: Volume 11313." Medical Imaging 2020: Image Processing (2020)

Synergizing medical imaging and radiotherapy with deep learning

Hongming Shan, Xun Jia, Pingkun Yan, Yunyao Li, Harald Paganetti, Ge Wang
2020 Machine Learning: Science and Technology  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (2.5 MB)
https://web.archive.org/web/20211014165045/https://iopscience.iop.org/article/10.1088/2632-2153/ab869f/pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL. The file type is application/pdf.

It is believed that deep learning in particular, and artificial intelligence and machine learning in general, will have a revolutionary potential to advance and synergize medical imaging and radiotherapy  ...  This article reviews deep learning methods for medical imaging (focusing on image reconstruction, segmentation, registration, and radiomics) and radiotherapy (ranging from planning and verification to  ...  Acknowledgment This work was partially support by NIH/NCI under award numbers R01CA233888, R01CA237267, R01CA227289, R37CA214639, and R01CA237269, and NIH/NIBIB under award number R01EB026646.  ... 
doi:10.1088/2632-2153/ab869f fatcat:aibfmfelcngkrk4ilwcs25c77a
DOAJ
Citation

Hongming Shan, Xun Jia, Pingkun Yan, Yunyao Li, Harald Paganetti, Ge Wang. "Synergizing medical imaging and radiotherapy with deep learning." Machine Learning: Science and Technology 1.2 (2020)

Dose Super-Resolution in Prostate Volumetric Modulated Arc Therapy Using Cascaded Deep Learning Networks

Dong-Seok Shin, Kyeong-Hyeon Kim, Sang-Won Kang, Seong-Hee Kang, Jae-Sung Kim, Tae-Ho Kim, Dong-Su Kim, Woong Cho, Tae Suk Suh, Jin-Beom Chung
2020 Frontiers in Oncology  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (2.3 MB)
https://web.archive.org/web/20201120120234/https://fjfsdata01prod.blob.core.windows.net/articles/files/593381/pubmed-zip/.versions/2/.package-entries/fonc-10-593381.pdf?sv=2018-03-28&sr=b&sig=F1Gjj5bF6a4CpxXROUn0gNhQALvOOitxFvRqLre%2F0l0%3D&se=2020-11-20T12%3A03%3A03Z&sp=r&rscd=attachment%3B%20filename%2A%3DUTF-8%27%27fonc-10-593381.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL. The file type is application/pdf.

Our cascaded network model consisted of a hierarchically densely connected U-net (HD U-net) and a residual dense network (RDN), which were trained separately following a two-dimensional slice-by-slice  ...  arc therapy plans for 73 prostate cancer patients.  ...  Hierarchically Densely Connected U-Net (HD U-Net) The HD U-net predicts the downsampled high-resolution dose (derived from the baseline dose with a 1 mm grid) using the lowresolution dose as the input.  ... 
doi:10.3389/fonc.2020.593381 pmid:33304852 pmcid:PMC7701297 fatcat:l62k7gtfx5bkta54pxgbj7uc4q
DOAJ
Citation

Dong-Seok Shin, Kyeong-Hyeon Kim, Sang-Won Kang, Seong-Hee Kang, Jae-Sung Kim, Tae-Ho Kim, Dong-Su Kim, Woong Cho, Tae Suk Suh, Jin-Beom Chung. "Dose Super-Resolution in Prostate Volumetric Modulated Arc Therapy Using Cascaded Deep Learning Networks." Frontiers in Oncology (2020) 593381

Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review [article]

Mingquan Lin, Jacob Wynne, Yang Lei, Tonghe Wang, Walter J. Curran, Tian Liu, Xiaofeng Yang
2021 arXiv   pre-print

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (438.7 kB)
https://web.archive.org/web/20210331225747/https://arxiv.org/pdf/2103.13588v1.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL. The file type is application/pdf.

Medical imaging is widely used in cancer diagnosis and treatment, and artificial intelligence (AI) has achieved tremendous success in various tasks of medical image analysis.  ...  A detailed review of each category is presented, highlighting important contributions and achievements. Specific challenges and potential AI applications in tumor subregion analysis are discussed.  ...  ACKNOWLEDGEMENTS This research was supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718 and Emory Winship Cancer Institute pilot grant.  ... 
arXiv:2103.13588v1 fatcat:3fxgny7u3bcxzcmlkhzz5fdvv4
Citation

Mingquan Lin, Jacob Wynne, Yang Lei, Tonghe Wang, Walter J. Curran, Tian Liu, Xiaofeng Yang. "Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review." arXiv (2021)

Medical Imaging Synthesis using Deep Learning and its Clinical Applications: A Review [article]

Tonghe Wang, Yang Lei, Yabo Fu, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print

Preserved Fulltext

Web Archive Capture PDF (474.3 kB)
https://web.archive.org/web/20200502003807/https://arxiv.org/pdf/2004.10322v1.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL. The file type is application/pdf.

performances with related clinical applications on representative studies.  ...  This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application.  ...  U-net As one of the first several studies employing deep learning in image synthesis, Han used CNN in synthesizing CT from MR images by adopting and modifying a U-net architecture.  ... 
arXiv:2004.10322v1 fatcat:bkhct7wzjnfrrd4kwa4rqw6rbe
Citation

Tonghe Wang, Yang Lei, Yabo Fu, Walter J. Curran, Tian Liu, Xiaofeng Yang. "Medical Imaging Synthesis using Deep Learning and its Clinical Applications: A Review." arXiv (2020)

Deep learning in medical imaging and radiation therapy

Berkman Sahiner, Aria Pezeshk, Lubomir M. Hadjiiski, Xiaosong Wang, Karen Drukker, Kenny H. Cha, Ronald M. Summers, Maryellen L. Giger
2018 Medical Physics (Lancaster)  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (962.4 kB)
https://web.archive.org/web/20201107143230/https://deepblue.lib.umich.edu/bitstream/handle/2027.42/146980/mp13264.pdf;jsessionid=898BC7425B3E4D4CA1503D3861BBADDD?sequence=2

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL. The file type is application/pdf.

The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies  ...  for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.  ...  Nguyen et al. 280 used a U-net to predict dose from patient image contours on prostate intensity-modulated radiation therapy (IMRT) patients and demonstrated desired radiation dose distributions.  ... 
doi:10.1002/mp.13264 pmid:30367497 fatcat:bottst5mvrbkfedbuocbrstcnm
Citation

Berkman Sahiner, Aria Pezeshk, Lubomir M. Hadjiiski, Xiaosong Wang, Karen Drukker, Kenny H. Cha, Ronald M. Summers, Maryellen L. Giger. "Deep learning in medical imaging and radiation therapy." Medical Physics (Lancaster) 46.1 (2018) e1-e36

A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging

Song Li, Yu-Qin Deng, Zhi-Ling Zhu, Hong-Li Hua, Ze-Zhang Tao
2021 Diagnostics  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (3.5 MB)
https://web.archive.org/web/20211208133610/https://mdpi-res.com/d_attachment/diagnostics/diagnostics-11-01523/article_deploy/diagnostics-11-01523-v3.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL. The file type is application/pdf.

In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC.  ...  Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal.  ...  Although NPC accounts for only 0.7% of all malignant tumours and is relatively rare compared with other cancers, it is one of the most common malignant tumours in head and neck cancer [2, 3] .  ... 
doi:10.3390/diagnostics11091523 pmid:34573865 pmcid:PMC8465998 fatcat:l6z7elk4sbeyvhii7m4p2wmf54
DOAJ
Citation

Song Li, Yu-Qin Deng, Zhi-Ling Zhu, Hong-Li Hua, Ze-Zhang Tao. "A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging." Diagnostics 11.9 (2021) 1523

Machine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist

Stefania Volpe, Matteo Pepa, Mattia Zaffaroni, Federica Bellerba, Riccardo Santamaria, Giulia Marvaso, Lars Johannes Isaksson, Sara Gandini, Anna Starzyńska, Maria Cristina Leonardi, Roberto Orecchia, Daniela Alterio (+1 others)
2021 Frontiers in Oncology  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (2.5 MB)
https://web.archive.org/web/20211127124353/https://fjfsdata01prod.blob.core.windows.net/articles/files/772663/pubmed-zip/.versions/2/.package-entries/fonc-11-772663.pdf?sv=2018-03-28&sr=b&sig=UyVzxvzxqckaOUIcbw25qX6aAjyZkm3Psm0Ijgat1oI%3D&se=2021-11-27T12%3A44%3A22Z&sp=r&rscd=attachment%3B%20filename%2A%3DUTF-8%27%27fonc-11-772663.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL. The file type is application/pdf.

and PurposeMachine learning (ML) is emerging as a feasible approach to optimize patients' care path in Radiation Oncology.  ...  The purpose of this clinically oriented systematic review is to illustrate the potential and limitations of the most commonly used ML models in solving everyday clinical issues in head and neck cancer  ...  (76) and focused on the use of a hierarchically densely connected U-net architecture (HD U-net) to predict three-dimensional dose distribution for the planning target volume and 22 OARs in a retrospectively  ... 
doi:10.3389/fonc.2021.772663 pmid:34869010 pmcid:PMC8637856 fatcat:gjlgknmscfbknp2npt3yzs5wvu
DOAJ
Citation

Stefania Volpe, Matteo Pepa, Mattia Zaffaroni, Federica Bellerba, Riccardo Santamaria, Giulia Marvaso, Lars Johannes Isaksson, Sara Gandini, Anna Starzyńska, Maria Cristina Leonardi, Roberto Orecchia, Daniela Alterio, Barbara Alicja Jereczek-Fossa. "Machine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist." Frontiers in Oncology (2021) 772663

Deep Learning in Medical Image Registration: A Review [article]

Yabo Fu, Yang Lei, Tonghe Wang, Walter J. Curran, Tian Liu, Xiaofeng Yang
2019 arXiv   pre-print

Preserved Fulltext

Web Archive Capture PDF (1.3 MB)
https://web.archive.org/web/20200321044106/https://arxiv.org/pdf/1912.12318v1.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL. The file type is application/pdf.

This paper presents a review of deep learning (DL) based medical image registration methods.  ...  Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of development in medical image registration using deep learning.  ...  Award, a philanthropic award provided by the Winship Cancer Institute of Emory University.  ... 
arXiv:1912.12318v1 fatcat:kuvckosqd5hp7asg6dofhuiis4
Citation

Yabo Fu, Yang Lei, Tonghe Wang, Walter J. Curran, Tian Liu, Xiaofeng Yang. "Deep Learning in Medical Image Registration: A Review." arXiv (2019)

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions [article]

Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir
2021 arXiv   pre-print

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (4.9 MB)
https://web.archive.org/web/20210722193336/https://arxiv.org/pdf/2107.09543v1.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL. The file type is application/pdf.

We provide a critical appraisal of the existing literature of GANs applied to cancer imagery, together with suggestions on future research directions to address these challenges.  ...  With this work, we strive to bridge the gap between the needs of the clinical cancer imaging community and the current and prospective research on GANs in the artificial intelligence community.  ...  Acknowledgments This project has received funding from the European Union's Horizon 2020 research and innovation pro-  ... 
arXiv:2107.09543v1 fatcat:jz76zqklpvh67gmwnsdqzgq5he
Citation

Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir. "A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions." arXiv (2021)

« Previous Showing results 1 — 15 out of 64 results