3 citations · 7 across the 10 of their papers we have counts for
10 papers
An Adaptive Purification Controller for Quantum Networks: Dynamic Protocol Selection and Multipartite Distillation
Pranav Kulkarni, Leo Sünkel, Michael Kölle
Efficient entanglement distribution is a cornerstone of the Quantum Internet. However, physical link parameters such as photon loss, memory coherence time, and gate error rates flu…
Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations
Pranav Kulkarni, Adway Kanhere, Dharmam Savani +4
Curating annotations for medical image segmentation is a labor-intensive and time-consuming task that requires domain expertise, resulting in "narrowly" focused deep learning (DL)…
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…
From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning
Pranav Kulkarni, Adway Kanhere, Paul H. Yi +1
Chest X-ray (CXR) datasets hosted on Kaggle, though useful from a data science competition standpoint, have limited utility in clinical use because of their narrow focus on diagnos…
An Interpretable Generative Model for Handwritten Digit Image Synthesis
Yao Zhu, Saksham Suri, Pranav Kulkarni +3
An interpretable generative model for handwritten digits synthesis is proposed in this work. Modern image generative models, such as Generative Adversarial Networks (GANs) and Vari…