41 citations · 50 across the 5 of their papers we have counts for
5 papers
Unified Supervision For Vision-Language Modeling in 3D Computed Tomography
Hao-Chih Lee, Zelong Liu, Hamza Ahmed +6
General-purpose vision-language models (VLMs) have emerged as promising tools in radiology, offering zero-shot capabilities that mitigate the need for large labeled datasets. Howev…
VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and Classification
Zelong Liu, Andrew Tieu, Nikhil Patel +9
Artificial Intelligence (AI) has the potential to revolutionize diagnosis and segmentation in medical imaging. However, development and clinical implementation face multiple challe…
MRAnnotator: multi-Anatomy and many-Sequence MRI segmentation of 44 structures
Alexander Zhou, Zelong Liu, Andrew Tieu +16
In this retrospective study, we annotated 44 structures on two datasets: an internal dataset of 1,518 MRI sequences from 843 patients at the Mount Sinai Health System, and an exter…
RadImageGAN -- A Multi-modal Dataset-Scale Generative AI for Medical Imaging
Zelong Liu, Alexander Zhou, Arnold Yang +10
Deep learning in medical imaging often requires large-scale, high-quality data or initiation with suitably pre-trained weights. However, medical datasets are limited by data availa…
Generative AI for Medical Imaging: extending the MONAI Framework
Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot +21
Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to he…