3 citations · 7 across the 10 of their papers we have counts for
6 papers · 1 filter
Towards Resource-Efficient Streaming of Large-Scale Medical Image Datasets for Deep Learning
Pranav Kulkarni, Adway Kanhere, Eliot Siegel +2
Large-scale medical imaging datasets have accelerated deep learning (DL) for medical image analysis. However, the large scale of these datasets poses a challenge for researchers, r…
ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging
Pranav Kulkarni, Sean Garin, Adway Kanhere +3
As the adoption of Artificial Intelligence (AI) systems within the clinical environment grows, limitations in bandwidth and compute can create communication bottlenecks when stream…
Text2Cohort: Facilitating Intuitive Access to Biomedical Data with Natural Language Cohort Discovery
Pranav Kulkarni, Adway Kanhere, Paul H. Yi +1
The Imaging Data Commons (IDC) is a cloud-based database that provides researchers with open access to cancer imaging data, with the goal of facilitating collaboration. However, co…
Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels
Pranav Kulkarni, Adway Kanhere, Paul H. Yi +1
Numerous large-scale chest x-ray datasets have spearheaded expert-level detection of abnormalities using deep learning. However, these datasets focus on detecting a subset of disea…
Exploring Semantic Perturbations on Grover
Ziqing Ji, Pranav Kulkarni, Marko Neskovic +2
With news and information being as easy to access as they currently are, it is more important than ever to ensure that people are not mislead by what they read. Recently, the rise…
SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations
Adway U. Kanhere, Pranav Kulkarni, Paul H. Yi +1
Segmentation is one of the most primary tasks in deep learning for medical imaging, owing to its multiple downstream clinical applications. However, generating manual annotations f…