activity
20192022
most citedLearning-Based Cost Functions for 3D and 4D Multi-Surface Multi-Object Segmentation of Knee MRI: Data from the Osteoarthritis Initiative

49 citations · 125 across the 9 of their papers we have counts for

collaborators

14 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.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

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…

cs.CV2020

Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Development

Alexandros Karargyris, Satyananda Kashyap, Ismini Lourentzou +8

We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye tracking system while a radiologist…

eess.IV2020

Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets

Sandesh Ghimire, Satyananda Kashyap, Joy T. Wu +2

Chest radiography is the most common medical image examination for screening and diagnosis in hospitals. Automatic interpretation of chest X-rays at the level of an entry-level rad…

cs.CV202015 cited

Looking in the Right place for Anomalies: Explainable AI through Automatic Location Learning

Satyananda Kashyap, Alexandros Karargyris, Joy Wu +5

Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging. Their 'black box' way of classifying medical images into anomaly labels poses…