activity
20172022
most citedPredicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning

1.9k citations · 5.9k across the 23 of their papers we have counts for

collaborators

23 papers

eess.IV2022★ 2 cited

Discovering novel systemic biomarkers in photos of the external eye

Boris Babenko, Ilana Traynis, Christina Chen +16

External eye photos were recently shown to reveal signs of diabetic retinal disease and elevated HbA1c. In this paper, we evaluate if external eye photos contain information about…

cs.CV2022★ 32 cited

Robust and Efficient Medical Imaging with Self-Supervision

Shekoofeh Azizi, Laura Culp, Jan Freyberg +31

Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach clinical expert level performance. However, such systems tend to demonstrate sub-optima…

cs.LG2022★ 2 cited

AI system for fetal ultrasound in low-resource settings

Ryan G. Gomes, Bellington Vwalika, Chace Lee +26

Despite considerable progress in maternal healthcare, maternal and perinatal deaths remain high in low-to-middle income countries. Fetal ultrasound is an important component of ant…

eess.IV2021★ 1 cited

Deep learning for detecting pulmonary tuberculosis via chest radiography: an international study across 10 countries

Sahar Kazemzadeh, Jin Yu, Shahar Jamshy +21

Tuberculosis (TB) is a top-10 cause of death worldwide. Though the WHO recommends chest radiographs (CXRs) for TB screening, the limited availability of CXR interpretation is a bar…

cs.CV2020★ 50 cited

Predicting Prostate Cancer-Specific Mortality with A.I.-based Gleason Grading

Ellery Wulczyn, Kunal Nagpal, Matthew Symonds +20

Gleason grading of prostate cancer is an important prognostic factor but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial i…

q-bio.GN2020

Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology

Babak Alipanahi, Farhad Hormozdiari, Babak Behsaz +10

Genome-wide association studies (GWAS) require accurate cohort phenotyping, but expert labeling can be costly, time-intensive, and variable. Here we develop a machine learning (ML)…