activity
20182022
most citedChest ImaGenome Dataset for Clinical Reasoning

26 citations · 55 across the 9 of their papers we have counts for

collaborators

19 papers

eess.IV20226 cited

Towards Automatic Prediction of Outcome in Treatment of Cerebral Aneurysms

Ashutosh Jadhav, Satyananda Kashyap, Hakan Bulu +6

Intrasaccular flow disruptors treat cerebral aneurysms by diverting the blood flow from the aneurysm sac. Residual flow into the sac after the intervention is a failure that could…

cs.CV2022

CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays

Gaurang Karwande, Amarachi Mbakawe, Joy T. Wu +3

Despite the progress in utilizing deep learning to automate chest radiograph interpretation and disease diagnosis tasks, change between sequential Chest X-rays (CXRs) has received…

cs.CV202126 cited

Chest ImaGenome Dataset for Clinical Reasoning

Joy T. Wu, Nkechinyere N. Agu, Ismini Lourentzou +9

Despite the progress in automatic detection of radiologic findings from chest X-ray (CXR) images in recent years, a quantitative evaluation of the explainability of these models is…

cs.CV2021

AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray

Nkechinyere N. Agu, Joy T. Wu, Hanqing Chao +5

Radiologists usually observe anatomical regions of chest X-ray images as well as the overall image before making a decision. However, most existing deep learning models only look a…

cs.CV2021

Channel Scaling: A Scale-and-Select Approach for Transfer Learning

Ken C. L. Wong, Satyananda Kashyap, Mehdi Moradi

Transfer learning with pre-trained neural networks is a common strategy for training classifiers in medical image analysis. Without proper channel selections, this often results in…

stat.ME20211 cited

Statistical learning and cross-validation for point processes

Ottmar Cronie, Mehdi Moradi, Christophe A. N. Biscio

This paper presents the first general (supervised) statistical learning framework for point processes in general spaces. Our approach is based on the combination of two new concept…