no code implementations • 21 Oct 2020 • Xie Chen, Sarangarajan Parthasarathy, William Gale, Shuangyu Chang, Michael Zeng
The context information is captured by the hidden states of LSTM-LMs across utterance and can be used to guide the first-pass search effectively.
no code implementations • 11 Nov 2019 • Sarangarajan Parthasarathy, William Gale, Xie Chen, George Polovets, Shuangyu Chang
We conduct language modeling and speech recognition experiments on the publicly available LibriSpeech corpus.
no code implementations • 8 Nov 2018 • Marcus A. Badgeley, John R. Zech, Luke Oakden-Rayner, Benjamin S. Glicksberg, Manway Liu, William Gale, Michael V. McConnell, Beth Percha, Thomas M. Snyder, Joel T. Dudley
In this study, we trained deep learning models on 17, 587 radiographs to classify fracture, five patient traits, and 14 hospital process variables.
no code implementations • 1 Jun 2018 • William Gale, Luke Oakden-Rayner, Gustavo Carneiro, Andrew P. Bradley, Lyle J. Palmer
Current approaches to explaining the decisions of deep learning systems for medical tasks have focused on visualising the elements that have contributed to each decision.
no code implementations • 17 Nov 2017 • William Gale, Luke Oakden-Rayner, Gustavo Carneiro, Andrew P. Bradley, Lyle J. Palmer
We developed an automated deep learning system to detect hip fractures from frontal pelvic x-rays, an important and common radiological task.