Zobrazeno 1 - 10
of 190
pro vyhledávání: '"Purdie, Thomas G"'
Multi-task learning (MTL) is a powerful approach in deep learning that leverages the information from multiple tasks during training to improve model performance. In medical imaging, MTL has shown great potential to solve various tasks. However, exis
Externí odkaz:
http://arxiv.org/abs/2309.03837
Objective: Machine learning (ML) based radiation treatment (RT) planning addresses the iterative and time-consuming nature of conventional inverse planning. Given the rising importance of Magnetic resonance (MR) only treatment planning workflows, we
Externí odkaz:
http://arxiv.org/abs/2203.03576
Autor:
Dawson, Laura A *, Ringash, Jolie, Fairchild, Alysa, Stos, Paul, Dennis, Kristopher, Mahmud, Aamer, Stuckless, Teri Lynn, Vincent, Francois, Roberge, David, Follwell, Matthew, Wong, Raimond K W, Jonker, Derek J, Knox, Jennifer J, Zimmermann, Camilla, Wong, Philip, Barry, Aisling S, Gaudet, Marc, Wong, Rebecca K S, Purdie, Thomas G, Tu, Dongsheng, O'Callaghan, Christopher J
Publikováno v:
In The Lancet Oncology October 2024 25(10):1337-1346
Publikováno v:
In Physics and Imaging in Radiation Oncology October 2024 32
Autor:
Babier, Aaron, Mahmood, Rafid, Zhang, Binghao, Alves, Victor G. L., Barragán-Montero, Ana Maria, Beaudry, Joel, Cardenas, Carlos E., Chang, Yankui, Chen, Zijie, Chun, Jaehee, Diaz, Kelly, Eraso, Harold David, Faustmann, Erik, Gaj, Sibaji, Gay, Skylar, Gronberg, Mary, Guo, Bingqi, He, Junjun, Heilemann, Gerd, Hira, Sanchit, Huang, Yuliang, Ji, Fuxin, Jiang, Dashan, Giraldo, Jean Carlo Jimenez, Lee, Hoyeon, Lian, Jun, Liu, Shuolin, Liu, Keng-Chi, Marrugo, José, Miki, Kentaro, Nakamura, Kunio, Netherton, Tucker, Nguyen, Dan, Nourzadeh, Hamidreza, Osman, Alexander F. I., Peng, Zhao, Muñoz, José Darío Quinto, Ramsl, Christian, Rhee, Dong Joo, Rodriguez, Juan David, Shan, Hongming, Siebers, Jeffrey V., Soomro, Mumtaz H., Sun, Kay, Hoyos, Andrés Usuga, Valderrama, Carlos, Verbeek, Rob, Wang, Enpei, Willems, Siri, Wu, Qi, Xu, Xuanang, Yang, Sen, Yuan, Lulin, Zhu, Simeng, Zimmermann, Lukas, Moore, Kevin L., Purdie, Thomas G., McNiven, Andrea L., Chan, Timothy C. Y.
We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP
Externí odkaz:
http://arxiv.org/abs/2202.08303
Intensity-modulated radiation therapy (IMRT) allows for the design of customized, highly-conformal treatments for cancer patients. Creating IMRT treatment plans, however, is a mathematically complex process, which is often tackled in multiple, simple
Externí odkaz:
http://arxiv.org/abs/2111.04847
Autor:
Babier, Aaron, Zhang, Binghao, Mahmood, Rafid, Moore, Kevin L., Purdie, Thomas G., McNiven, Andrea L., Chan, Timothy C. Y.
The purpose of this work is to advance fair and consistent comparisons of dose prediction methods for knowledge-based planning (KBP) in radiation therapy research. We hosted OpenKBP, a 2020 AAPM Grand Challenge, and challenged participants to develop
Externí odkaz:
http://arxiv.org/abs/2011.14076
Autor:
Mayo, Charles S. *, Feng, Mary U., Brock, Kristy K., Kudner, Randi, Balter, Peter, Buchsbaum, Jeffrey C., Caissie, Amanda, Covington, Elizabeth *, Daugherty, Emily C., Dekker, Andre L., Fuller, Clifton D., Hallstrom, Anneka L., Hong, David S., Hong, Julian C., Kamran, Sophia C., Katsoulakis, Eva, Kildea, John, Krauze, Andra V., Kruse, Jon J., McNutt, Tod, Mierzwa, Michelle *, Moreno, Amy, Palta, Jatinder R., Popple, Richard, Purdie, Thomas G., Richardson, Susan, Sharp, Gregory C., Satomi, Shiraishi, Tarbox, Lawrence R., Venkatesan, Aradhana M., Witztum, Alon, Woods, Kelly E., Yao, Yuan *, Farahani, Keyvan, Aneja, Sanjay, Gabriel, Peter E., Hadjiiski, Lubomire *, Ruan, Dan, Siewerdsen, Jeffrey H., Bratt, Steven, Casagni, Michelle, Chen, Su, Christodouleas, John C., DiDonato, Anthony, Hayman, James *, Kapoor, Rishhab, Kravitz, Saul, Sebastian, Sharon, Von Siebenthal, Martin, Bosch, Walter, Hurkmans, Coen, Yom, Sue S., Xiao, Ying
Publikováno v:
In International Journal of Radiation Oncology, Biology, Physics 1 November 2023 117(3):533-550
Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present an atlas-based approach w
Externí odkaz:
http://arxiv.org/abs/1609.00740
Autor:
McIntosh, Chris, Purdie, Thomas G.
Automating the radiotherapy treatment planning process is a technically challenging problem. The majority of automated approaches have focused on customizing and inferring dose volume objectives to used in plan optimization. In this work we outline a
Externí odkaz:
http://arxiv.org/abs/1608.04330