Zobrazeno 1 - 10
of 248
pro vyhledávání: '"McIntosh, Chris"'
Medical vision-language pretraining models (VLPM) have achieved remarkable progress in fusing chest X-rays (CXR) with clinical texts, introducing image-text data binding approaches that enable zero-shot learning and downstream clinical tasks. However
Externí odkaz:
http://arxiv.org/abs/2403.12894
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
Autor:
Yoo, Jay J., Namdar, Khashayar, Carey, Sean, Fischer, Sandra E., McIntosh, Chris, Khalvati, Farzad, Rogalla, Patrik
Objectives: To develop and evaluate a radiomics machine learning model for detecting liver fibrosis on CT of the liver. Methods: For this retrospective, single-centre study, radiomic features were extracted from Regions of Interest (ROIs) on CT image
Externí odkaz:
http://arxiv.org/abs/2211.14396
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
Publikováno v:
In Physics and Imaging in Radiation Oncology October 2024 32
Autor:
Marvasti, Tina Binesh, Gao, Yuan, Murray, Kevin R., Hershman, Steve, McIntosh, Chris, Moayedi, Yasbanoo
Publikováno v:
In Canadian Journal of Cardiology October 2024 40(10):1934-1945
Autor:
Tsang, Derek S. *, Tsui, Grace, Santiago, Anna T., Keller, Harald *, Purdie, Thomas, Mcintosh, Chris, Bauman, Glenn, La Macchia, Nancy, Parent, Amy, Dama, Hitesh, Ahmed, Sameera *, Laperriere, Normand *, Millar, Barbara-Ann *, Liu, Valerie *, Hodgson, David C. *
Publikováno v:
In International Journal of Radiation Oncology, Biology, Physics 1 August 2024 119(5):1429-1436
A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data
Autor:
Kazmierski, Michal, Welch, Mattea, Kim, Sejin, McIntosh, Chris, Head, Princess Margaret, Group, Neck Cancer, Rey-McIntyre, Katrina, Huang, Shao Hui, Patel, Tirth, Tadic, Tony, Milosevic, Michael, Liu, Fei-Fei, Hope, Andrew, Bratman, Scott, Haibe-Kains, Benjamin
Accurate prognosis for an individual patient is a key component of precision oncology. Recent advances in machine learning have enabled the development of models using a wider range of data, including imaging. Radiomics aims to extract quantitative p
Externí odkaz:
http://arxiv.org/abs/2101.11935
Autor:
Ly, Cathy Ong, Suszko, Adrian M., Denham, Nathan C., Chakraborty, Praloy, Rahimi, Mahbod, McIntosh, Chris, Chauhan, Vijay S.
Publikováno v:
In Heart Rhythm
Autor:
Haibe-Kains, Benjamin, Adam, George Alexandru, Hosny, Ahmed, Khodakarami, Farnoosh, Board, MAQC Society, Waldron, Levi, Wang, Bo, McIntosh, Chris, Kundaje, Anshul, Greene, Casey S., Hoffman, Michael M., Leek, Jeffrey T., Huber, Wolfgang, Brazma, Alvis, Pineau, Joelle, Tibshirani, Robert, Hastie, Trevor, Ioannidis, John P. A., Quackenbush, John, Aerts, Hugo J. W. L.
Publikováno v:
Nature 586 (2020) E14-E16
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and
Externí odkaz:
http://arxiv.org/abs/2003.00898