output
20222024
most citedFederated Learning Enables Big Data for Rare Cancer Boundary Detection

390 citations

5 papers

q-bio.QM2024★ 1 cited

AI prediction of cardiovascular events using opportunistic epicardial adipose tissue assessments from CT calcium score

Tao Hu, Joshua Freeze, Prerna Singh +8

Background: Recent studies have used basic epicardial adipose tissue (EAT) assessments (e.g., volume and mean HU) to predict risk of atherosclerosis-related, major adverse cardiova…

q-bio.QM2023★ 29 cited

Enhancing cardiovascular risk prediction through AI-enabled calcium-omics

Ammar Hoori, Sadeer Al-Kindi, Tao Hu +8

Background. Coronary artery calcium (CAC) is a powerful predictor of major adverse cardiovascular events (MACE). Traditional Agatston score simply sums the calcium, albeit in a non…

q-bio.QM2022★ 41 cited

Novel Radiomic Measurements of Tumor- Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers

Nathaniel Braman, Prateek Prasanna, Kaustav Bera +14

Purpose: Tumor-associated vasculature differs from healthy blood vessels by its chaotic architecture and twistedness, which promotes treatment resistance. Measurable differences in…

physics.med-ph2022★ 5 cited

Improved accuracy and reproducibility of coronary artery cal-cification features using deconvolution

Yingnan Song, Ammar Hoori, Hao Wu +7

Our long-range goal is to improve current whole-heart CT calcium score by extracting quantitative features from individual calcifications. We performed deconvolution to improve sma…

cs.LG2022★ 390 cited

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Sarthak Pati, Ujjwal Baid, Brandon Edwards +276

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…