Zobrazeno 1 - 10
of 793
pro vyhledávání: '"Steve B Jiang"'
Autor:
Biling Wang, Michael Dohopolski, Ti Bai, Junjie Wu, Raquibul Hannan, Neil Desai, Aurelie Garant, Daniel Yang, Dan Nguyen, Mu-Han Lin, Robert Timmerman, Xinlei Wang, Steve B Jiang
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 2, p 025077 (2024)
Our study aims to explore the long-term performance patterns for deep learning (DL) models deployed in clinic and to investigate their efficacy in relation to evolving clinical practices. We conducted a retrospective study simulating the clinical imp
Externí odkaz:
https://doaj.org/article/c92535c3d5444875b45fe41667566894
Autor:
Luo Ouyang, Michael Folkerts, You Zhang, Brian Hrycushko, Richard Lamphier, Pam Lee, Eric Chambers, Ezequiel Ramirez, Robert Reynolds, Yulong Yan, Steve B. Jiang, Robert Timmerman, Neil Desai, Ramzi Abdulrahman, Xuejun Gu
Publikováno v:
Physics and Imaging in Radiation Oncology, Vol 4, Iss , Pp 22-25 (2017)
An indexed rotational immobilization system was developed for supine total body irradiation (TBI). Treatment plans had multi-isocentric volumetric modulated arc therapy (VMAT) beams to the upper body and parallel-opposed fields to the lower body, wit
Externí odkaz:
https://doaj.org/article/f2cb6b1ec0454f379defa61d210fdeb5
Autor:
Tsuicheng D Chiu, Tatsuya J Arai, James Campbell Iii, Steve B Jiang, Ralph P Mason, Strahinja Stojadinovic
Publikováno v:
PLoS ONE, Vol 13, Iss 5, p e0198065 (2018)
Multi-modality image-guided radiotherapy is the standard of care in contemporary cancer management; however, it is not common in preclinical settings due to both hardware and software limitations. Soft tissue lesions, such as orthotopic prostate tumo
Externí odkaz:
https://doaj.org/article/1b136ad632814b52bc4f9f3839c4fe35
Autor:
Yan Liu, Strahinja Stojadinovic, Brian Hrycushko, Zabi Wardak, Steven Lau, Weiguo Lu, Yulong Yan, Steve B Jiang, Xin Zhen, Robert Timmerman, Lucien Nedzi, Xuejun Gu
Publikováno v:
PLoS ONE, Vol 12, Iss 10, p e0185844 (2017)
Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segme
Externí odkaz:
https://doaj.org/article/348da4fdd06546beaed9bc92aa603f59
Publikováno v:
Journal of Artificial Intelligence for Medical Sciences. 2:62-75
The purpose of this study is to develop a deep learning based method that can automatically generate segmentations on cone-beam CT (CBCT) for head and neck online adaptive radiation therapy (ART), where expert-drawn contours in planning CT (pCT) can
Autor:
Mu-Han Lin, Howard E. Morgan, Neil Desai, Raquibul Hannan, Dan Nguyen, Steve B. Jiang, Anjali Balagopal, Aurelie Garant, Maryam Mashayekhi
Publikováno v:
Journal of Artificial Intelligence for Medical Sciences. 2:85-96
Autor:
Young Suk Kwon, Michael Dohopolski, Howard Morgan, Aurelie Garant, David Sher, Asal Rahimi, Nina N. Sanford, Dat T. Vo, Kevin Albuquerque, Kiran Kumar, Robert Timmerman, Steve B. Jiang
Publikováno v:
Practical radiation oncology. 13(1)
Publikováno v:
Med Phys
Purpose Cone-beam computed tomography (CBCT) scanning is used daily or weekly (i.e., on-treatment CBCT) for accurate patient setup in image-guided radiotherapy. However, inaccuracy of CT numbers prevents CBCT from performing advanced tasks such as do
Autor:
Ana M. Barragan-Montero, Nima Hassan Rezaeian, Steve B. Jiang, Sebastiaan Breedveld, Roya Norouzi Kandalan, Mu-Han Lin, Kamesh Namuduri, Dan Nguyen
Publikováno v:
Radiother Oncol
Radiotherapy and Oncology, 153, 228-235. Elsevier Ireland Ltd
Radiotherapy and Oncology, 153, 228-235. Elsevier Ireland Ltd
Purpose This work aims to study the generalizability of a pre-developed deep learning (DL) dose prediction model for volumetric modulated arc therapy (VMAT) for prostate cancer and to adapt the model, via transfer learning with minimal input data, to
Publikováno v:
IEEE J Biomed Health Inform
Objective: accurately classifying the malignancy of lesions detected in a screening scan is critical for reducing false positives. Radiomics holds great potential to differentiate malignant from benign tumors by extracting and analyzing a large numbe