173 citations · 302 across the 13 of their papers we have counts for
13 papers · 1 filter
Basis Scaling and Double Pruning for Efficient Inference in Network-Based Transfer Learning
Ken C. L. Wong, Satyananda Kashyap, Mehdi Moradi
Network-based transfer learning allows the reuse of deep learning features with limited data, but the resulting models can be unnecessarily large. Although network pruning can impr…
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…
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…
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…
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…
Chest X-ray Report Generation through Fine-Grained Label Learning
Tanveer Syeda-Mahmood, Ken C. L. Wong, Yaniv Gur +9
Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals. However, the…
Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification
Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris +1
Automated diagnostic assistants in healthcare necessitate accurate AI models that can be trained with limited labeled data, can cope with severe class imbalances and can support si…
Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation
Jayaraman J. Thiagarajan, Satyananda Kashyap, Alexandros Karagyris
Weakly supervised instance labeling using only image-level labels, in lieu of expensive fine-grained pixel annotations, is crucial in several applications including medical image a…
Building a Benchmark Dataset and Classifiers for Sentence-Level Findings in AP Chest X-rays
Tanveer Syeda-Mahmood, Hassan M. Ahmad, Nadeem Ansari +9
Chest X-rays are the most common diagnostic exams in emergency rooms and hospitals. There has been a surge of work on automatic interpretation of chest X-rays using deep learning a…
Just-Enough Interaction Approach to Knee MRI Segmentation: Data from the Osteoarthritis Initiative
Satyananda Kashyap, Honghai Zhang, Milan Sonka
State-of-the-art automated segmentation algorithms are not 100\% accurate especially when segmenting difficult to interpret datasets like those with severe osteoarthritis (OA). We…
Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative
Satyananda Kashyap, Ipek Oguz, Honghai Zhang +1
We present a fully automated learning-based approach for segmenting knee cartilage in the presence of osteoarthritis (OA). The algorithm employs a hierarchical set of two random fo…
Learning-Based Cost Functions for 3D and 4D Multi-Surface Multi-Object Segmentation of Knee MRI: Data from the Osteoarthritis Initiative
Satyananda Kashyap, Honghai Zhang, Karan Rao +1
A fully automated knee MRI segmentation method to study osteoarthritis (OA) was developed using a novel hierarchical set of random forests (RF) classifiers to learn the appearance…
Age prediction using a large chest X-ray dataset
Alexandros Karargyris, Satyananda Kashyap, Joy T Wu +3
Age prediction based on appearances of different anatomies in medical images has been clinically explored for many decades. In this paper, we used deep learning to predict a person…