High-throughput omics profiling advancements have greatly enhanced cancer patient stratification.
Deployed machine learning models should be updated to take advantage of a larger sample size to improve performance, as more data is gathered over time.
The Cox proportional hazards model is a canonical method in survival analysis for prediction of the life expectancy of a patient given clinical or genetic covariates -- it is a linear model in its original form.
Predicting outcomes, such as survival or metastasis for individual cancer patients is a crucial component of precision oncology.
no code implementations • 28 Jan 2021 • Michal Kazmierski, Mattea Welch, Sejin Kim, Chris McIntosh, Princess Margaret Head, Neck Cancer Group, Katrina Rey-McIntyre, Shao Hui Huang, Tirth Patel, Tony Tadic, Michael Milosevic, Fei-Fei Liu, Andrew Hope, Scott Bratman, Benjamin Haibe-Kains
We have conducted an institutional machine learning challenge to develop an accurate model for overall survival prediction in head and neck cancer using clinical data etxracted from electronic medical records and pre-treatment radiological images, as well as to evaluate the true added benefit of radiomics for head and neck cancer prognosis.
Accurate survival prediction is crucial for development of precision cancer medicine, creating the need for new sources of prognostic information.
In this work, we apply a transfer learning approach to improve predictive power in noisy data systems with large variable confidence datasets.
1 code implementation • 6 May 2020 • Tahereh Javaheri, Morteza Homayounfar, Zohreh Amoozgar, Reza Reiazi, Fatemeh Homayounieh, Engy Abbas, Azadeh Laali, Amir Reza Radmard, Mohammad Hadi Gharib, Seyed Ali Javad Mousavi, Omid Ghaemi, Rosa Babaei, Hadi Karimi Mobin, Mehdi Hosseinzadeh, Rana Jahanban-Esfahlan, Khaled Seidi, Mannudeep K. Kalra, Guanglan Zhang, L. T. Chitkushev, Benjamin Haibe-Kains, Reza Malekzadeh, Reza Rawassizadeh
In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format.
Ranked #1 on COVID-19 Diagnosis on
no code implementations • 28 Feb 2020 • Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, MAQC Society Board, Levi Waldron, Bo wang, Chris McIntosh, Anshul Kundaje, Casey S. Greene, Michael M. Hoffman, Jeffrey T. Leek, Wolfgang Huber, Alvis Brazma, Joelle Pineau, Robert Tibshirani, Trevor Hastie, John P. A. Ioannidis, John Quackenbush, Hugo J. W. L. Aerts
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening.
We investigate the transferability of adversarial examples between models using the angle between the input-output Jacobians of different models.
Many deep learning algorithms can be easily fooled with simple adversarial examples.
We present two deep generative models based on Variational Autoencoders to improve the accuracy of drug response prediction.