156 citations · 182 across the 9 of their papers we have counts for
9 papers
Specializing Foundation Models via Mixture of Low-Rank Experts for Comprehensive Head CT Analysis
Youngjin Yoo, Han Liu, Bogdan Georgescu +14
Foundation models pre-trained on large-scale datasets demonstrate strong transfer learning capabilities; however, their adaptation to complex multi-label diagnostic tasks-such as c…
VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation
Badhan Kumar Das, Ajay Singh, Gengyan Zhao +5
Self-supervised pretrain techniques have been widely used to improve the downstream tasks' performance. However, real-world magnetic resonance (MR) studies usually consist of diffe…
Multi-Plane Vision Transformer for Hemorrhage Classification Using Axial and Sagittal MRI Data
Badhan Kumar Das, Gengyan Zhao, Boris Mailhe +4
Identifying brain hemorrhages from magnetic resonance imaging (MRI) is a critical task for healthcare professionals. The diverse nature of MRI acquisitions with varying contrasts a…
AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities
Badhan Kumar Das, Gengyan Zhao, Han Liu +4
Pretrain techniques, whether supervised or self-supervised, are widely used in deep learning to enhance model performance. In real-world clinical scenarios, different sets of magne…
A Non-contrast Head CT Foundation Model for Comprehensive Neuro-Trauma Triage
Youngjin Yoo, Bogdan Georgescu, Yanbo Zhang +14
Recent advancements in AI and medical imaging offer transformative potential in emergency head CT interpretation for reducing assessment times and improving accuracy in the face of…
Self Pre-training with Adaptive Mask Autoencoders for Variable-Contrast 3D Medical Imaging
Badhan Kumar Das, Gengyan Zhao, Han Liu +4
The Masked Autoencoder (MAE) has recently demonstrated effectiveness in pre-training Vision Transformers (ViT) for analyzing natural images. By reconstructing complete images from…
Machine Learning Automatically Detects COVID-19 using Chest CTs in a Large Multicenter Cohort
Eduardo Jose Mortani Barbosa, Bogdan Georgescu, Shikha Chaganti +15
Objectives: To investigate machine-learning classifiers and interpretable models using chest CT for detection of COVID-19 and differentiation from other pneumonias, ILD and normal…
3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19
Siqi Liu, Bogdan Georgescu, Zhoubing Xu +10
The Coronavirus Disease (COVID-19) has affected 1.8 million people and resulted in more than 110,000 deaths as of April 12, 2020. Several studies have shown that tomographic patter…
Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT
Shikha Chaganti, Abishek Balachandran, Guillaume Chabin +17
Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities…