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20162022
most citedDeep Learning to Quantify Pulmonary Edema in Chest Radiographs

42 citations · 62 across the 9 of their papers we have counts for

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Showing cs.CVShow all

6 papers · 1 filter

cs.CV2022★ 3 cited

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…

cs.CV2021

Image Classification with Consistent Supporting Evidence

Peiqi Wang, Ruizhi Liao, Daniel Moyer +3

Adoption of machine learning models in healthcare requires end users' trust in the system. Models that provide additional supportive evidence for their predictions promise to facil…

cs.CV2020★ 1 cited

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…

cs.CV2019★ 1 cited

Temporal Registration in Application to In-utero MRI Time Series

Ruizhi Liao, Esra A. Turk, Miaomiao Zhang +4

We present a robust method to correct for motion in volumetric in-utero MRI time series. Time-course analysis for in-utero volumetric MRI time series often suffers from substantial…

cs.CV2019★ 7 cited

Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images

Ruizhi Liao, Jonathan Rubin, Grace Lam +5

We propose and demonstrate machine learning algorithms to assess the severity of pulmonary edema in chest x-ray images of congestive heart failure patients. Accurate assessment of…

cs.CV2016★ 1 cited

Temporal Registration in In-Utero Volumetric MRI Time Series

Ruizhi Liao, Esra Turk, Miaomiao Zhang +4

We present a robust method to correct for motion and deformations for in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI requires robust alignment across…