Assessing Robustness to Noise: Low-Cost Head CT Triage

no code implementations • 17 Mar 2020 • Sarah M. Hooper, Jared A. Dunnmon, Matthew P. Lungren, Sanjiv Sam Gambhir, Christopher Ré, Adam S. Wang, Bhavik N. Patel

We then show that the trained model is robust to reduced tube current and fewer projections, with the AUROC dropping only 0. 65% for images acquired with a 16x reduction in tube current and 0. 22% for images acquired with 8x fewer projections.

Computed Tomography (CT) Image Classification +1

Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods

1 code implementation • ICML 2020 • Daniel Y. Fu, Mayee F. Chen, Frederic Sala, Sarah M. Hooper, Kayvon Fatahalian, Christopher Ré

In this work, we show that, for a class of latent variable models highly applicable to weak supervision, we can find a closed-form solution to model parameters, obviating the need for iterative solutions like stochastic gradient descent (SGD).