150 citations · 186 across the 8 of their papers we have counts for
9 papers
Training Large ASR Encoders with Differential Privacy
Geeticka Chauhan, Steve Chien, Om Thakkar +2
Self-supervised learning (SSL) methods for large speech models have proven to be highly effective at ASR. With the interest in public deployment of large pre-trained models, there…
Improving Medical Visual Representations via Radiology Report Generation
Keegan Quigley, Miriam Cha, Josh Barua +4
Vision-language pretraining has been shown to produce high-quality visual encoders which transfer efficiently to downstream computer vision tasks. Contrastive learning approaches h…
RadTex: Learning Efficient Radiograph Representations from Text Reports
Keegan Quigley, Miriam Cha, Ruizhi Liao +4
Automated analysis of chest radiography using deep learning has tremendous potential to enhance the clinical diagnosis of diseases in patients. However, deep learning models typica…
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View
Di Jin, Elena Sergeeva, Wei-Hung Weng +2
The increasing availability of large collections of electronic health record (EHR) data and unprecedented technical advances in deep learning (DL) have sparked a surge of research…
How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact
Zhijing Jin, Geeticka Chauhan, Brian Tse +2
Recent years have seen many breakthroughs in natural language processing (NLP), transitioning it from a mostly theoretical field to one with many real-world applications. Noting th…
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
Geeticka Chauhan, Ruizhi Liao, William Wells +6
We propose and demonstrate a novel machine learning algorithm that assesses pulmonary edema severity from chest radiographs. While large publicly available datasets of chest radiog…
REflex: Flexible Framework for Relation Extraction in Multiple Domains
Geeticka Chauhan, Matthew B. A. McDermott, Peter Szolovits
Systematic comparison of methods for relation extraction (RE) is difficult because many experiments in the field are not described precisely enough to be completely reproducible an…
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III
Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan +3
Robust machine learning relies on access to data that can be used with standardized frameworks in important tasks and the ability to develop models whose performance can be reasona…
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation
Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan +4
Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operat…