no code implementations • 11 Dec 2022 • Peiqi Wang, William M. Wells, Seth Berkowitz, Steven Horng, Polina Golland
Image-text multimodal representation learning aligns data across modalities and enables important medical applications, e. g., image classification, visual grounding, and cross-modal retrieval.
no code implementations • 5 Aug 2022 • Keegan Quigley, Miriam Cha, Ruizhi Liao, Geeticka Chauhan, Steven Horng, Seth Berkowitz, Polina Golland
In this paper, we build a data-efficient learning framework that utilizes radiology reports to improve medical image classification performance with limited labeled data (fewer than 1000 examples).
no code implementations • 13 Nov 2021 • Peiqi Wang, Ruizhi Liao, Daniel Moyer, Seth Berkowitz, Steven Horng, Polina Golland
We define consistent evidence to be both compatible and sufficient with respect to model predictions.
1 code implementation • 8 Mar 2021 • Ruizhi Liao, Daniel Moyer, Miriam Cha, Keegan Quigley, Seth Berkowitz, Steven Horng, Polina Golland, William M. Wells
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text.
1 code implementation • 22 Aug 2020 • Geeticka Chauhan, Ruizhi Liao, William Wells, Jacob Andreas, Xin Wang, Seth Berkowitz, Steven Horng, Peter Szolovits, Polina Golland
To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time.
1 code implementation • 13 Aug 2020 • Steven Horng, Ruizhi Liao, Xin Wang, Sandeep Dalal, Polina Golland, Seth J. Berkowitz
Results: The area under the receiver operating characteristic curve (AUC) for differentiating alveolar edema from no edema was 0. 99 for the semi-supervised model and 0. 87 for the pre-trained models.
1 code implementation • 29 Jul 2020 • Divya Gopinath, Monica Agrawal, Luke Murray, Steven Horng, David Karger, David Sontag
We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical documentation.
no code implementations • 2 Oct 2019 • Irene Y. Chen, Monica Agrawal, Steven Horng, David Sontag
Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge.
no code implementations • 27 Feb 2019 • Ruizhi Liao, Jonathan Rubin, Grace Lam, Seth Berkowitz, Sandeep Dalal, William Wells, Steven Horng, Polina Golland
We propose and demonstrate machine learning algorithms to assess the severity of pulmonary edema in chest x-ray images of congestive heart failure patients.
no code implementations • 21 Jan 2019 • Alistair E. W. Johnson, Tom J. Pollard, Nathaniel R. Greenbaum, Matthew P. Lungren, Chih-ying Deng, Yifan Peng, Zhiyong Lu, Roger G. Mark, Seth J. Berkowitz, Steven Horng
Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient's thorax, but requiring specialized training for proper interpretation.
no code implementations • 2 Aug 2016 • Yoni Halpern, Steven Horng, David Sontag
We describe a method for parameter estimation in bipartite probabilistic graphical models for joint prediction of clinical conditions from the electronic medical record.
no code implementations • 10 Nov 2015 • Yoni Halpern, Steven Horng, David Sontag
We present a semi-supervised learning algorithm for learning discrete factor analysis models with arbitrary structure on the latent variables.