22 citations · 22 across the 1 of their papers we have counts for
6 papers
Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning
Pranav Kulkarni, Adway Kanhere, Harshita Kukreja +3
Generative Adversarial Network (GAN)-based synthesis of fat suppressed (FS) MRIs from non-FS proton density sequences has the potential to accelerate acquisition of knee MRIs. Howe…
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)…
Hidden in Plain Sight: Undetectable Adversarial Bias Attacks on Vulnerable Patient Populations
Pranav Kulkarni, Andrew Chan, Nithya Navarathna +3
The proliferation of artificial intelligence (AI) in radiology has shed light on the risk of deep learning (DL) models exacerbating clinical biases towards vulnerable patient popul…
A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments
Guangyao Zheng, Shuhao Lai, Vladimir Braverman +2
While Deep Reinforcement Learning has been widely researched in medical imaging, the training and deployment of these models usually require powerful GPUs. Since imaging environmen…
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…
Cross-Domain Federated Learning in Medical Imaging
Vishwa S Parekh, Shuhao Lai, Vladimir Braverman +4
Federated learning is increasingly being explored in the field of medical imaging to train deep learning models on large scale datasets distributed across different data centers wh…