54 citations · 144 across the 9 of their papers we have counts for
7 papers · 1 filter
CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray
Mingquan Lin, Gregory Holste, Song Wang +30
The CXR-LT series is a community-driven initiative designed to enhance lung disease classification using chest X-rays (CXR). It tackles challenges in open long-tailed lung disease…
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling
Gregory Holste, Mingquan Lin, Ruiwen Zhou +9
Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classificat…
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
Gregory Holste, Yiliang Zhou, Song Wang +22
Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more rela…
How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?
Gregory Holste, Ziyu Jiang, Ajay Jaiswal +12
Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. Howe…
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study
Gregory Holste, Song Wang, Ziyu Jiang +5
Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings. While a trained radiologist can learn the visual pre…
Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis
Gregory Holste, Evangelos K. Oikonomou, Bobak J. Mortazavi +2
Advances in self-supervised learning (SSL) have shown that self-supervised pretraining on medical imaging data can provide a strong initialization for downstream supervised classif…
Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays
Yan Han, Gregory Holste, Ying Ding +3
Before the recent success of deep learning methods for automated medical image analysis, practitioners used handcrafted radiomic features to quantitatively describe local patches o…