43 citations · 72 across the 7 of their papers we have counts for
7 papers
A Comparative Study of Open-Source Large Language Models, GPT-4 and Claude 2: Multiple-Choice Test Taking in Nephrology
Sean Wu, Michael Koo, Lesley Blum +4
In recent years, there have been significant breakthroughs in the field of natural language processing, particularly with the development of large language models (LLMs). These LLM…
BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis
Masoud Monajatipoor, Mozhdeh Rouhsedaghat, Liunian Harold Li +4
Vision-and-language(V&L) models take image and text as input and learn to capture the associations between them. Prior studies show that pre-trained V&L models can significantly im…
Nonlinear Schrödinger Kernel for hardware acceleration in machine learning inference
Tingyi Zhou, Fabien Scalzo, Bahram Jalali
Alternative machine learning approaches that are computationally light with low latency and can work with only a small training dataset are needed for applications where the insati…
Intra-Domain Task-Adaptive Transfer Learning to Determine Acute Ischemic Stroke Onset Time
Haoyue Zhang, Jennifer S Polson, Kambiz Nael +6
Treatment of acute ischemic strokes (AIS) is largely contingent upon the time since stroke onset (TSS). However, TSS may not be readily available in up to 25% of patients with unwi…
Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography
Osama N. Hassan, Serhat Sahin, Vahid Mohammadzadeh +9
The estimation of glaucoma progression is a challenging task as the rate of disease progression varies among individuals in addition to other factors such as measurement variabilit…
Prostate cancer inference via weakly-supervised learning using a large collection of negative MRI
Ruiming Cao, Xinran Zhong, Fabien Scalzo +2
Recent advances in medical imaging techniques have led to significant improvements in the management of prostate cancer (PCa). In particular, multi-parametric MRI (mp-MRI) continue…
Synthetic Perfusion Maps: Imaging Perfusion Deficits in DSC-MRI with Deep Learning
Andreas Hess, Raphael Meier, Johannes Kaesmacher +5
In this work, we present a novel convolutional neural net- work based method for perfusion map generation in dynamic suscepti- bility contrast-enhanced perfusion imaging. The propo…