6 papers
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…
Towards Fair Medical AI: Adversarial Debiasing of 3D CT Foundation Embeddings
Guangyao Zheng, Michael A. Jacobs, Vladimir Braverman +1
Self-supervised learning has revolutionized medical imaging by enabling efficient and generalizable feature extraction from large-scale unlabeled datasets. Recently, self-supervise…
Demographic Predictability in 3D CT Foundation Embeddings
Guangyao Zheng, Michael A. Jacobs, Vishwa S. Parekh
Self-supervised foundation models have recently been successfully extended to encode three-dimensional (3D) computed tomography (CT) images, with excellent performance across sever…
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…
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…