3 citations · 7 across the 12 of their papers we have counts for
12 papers
UNIStainNet: Foundation-Model-Guided Virtual Staining of H&E to IHC
Jillur Rahman Saurav, Thuong Le Hoai Pham, Pritam Mukherjee +3
Virtual immunohistochemistry (IHC) staining from hematoxylin and eosin (H&E) images can accelerate diagnostics by providing preliminary molecular insight directly from routine sect…
Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification
Skylar Chan, Pranav Kulkarni, Paul H. Yi +1
Quantum machine learning (QML) has the potential for improving the multi-label classification of rare, albeit critical, diseases in large-scale chest x-ray (CXR) datasets due to th…
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)…
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…
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 Isolation to Collaboration: Federated Class-Heterogeneous Learning for Chest X-Ray Classification
Pranav Kulkarni, Adway Kanhere, Paul H. Yi +1
Federated learning (FL) is a promising paradigm to collaboratively train a global chest x-ray (CXR) classification model using distributed datasets while preserving patient privacy…
Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT
Jacopo Teneggi, Paul H. Yi, Jeremias Sulam
Modern machine learning pipelines, in particular those based on deep learning (DL) models, require large amounts of labeled data. For classification problems, the most common learn…
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…