Predicting outcomes, such as survival or metastasis for individual cancer patients is a crucial component of precision oncology.
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.
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
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.