53 citations · 134 across the 13 of their papers we have counts for
14 papers
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…
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging
Guangyao Zheng, Michael A. Jacobs, Vladimir Braverman +1
Federated learning is a recent development in the machine learning area that allows a system of devices to train on one or more tasks without sharing their data to a single locatio…
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging
Guangyao Zheng, Samson Zhou, Vladimir Braverman +2
Selective experience replay is a popular strategy for integrating lifelong learning with deep reinforcement learning. Selective experience replay aims to recount selected experienc…
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…
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…