9 citations · 28 across the 7 of their papers we have counts for
7 papers
CheXpert Plus: Augmenting a Large Chest X-ray Dataset with Text Radiology Reports, Patient Demographics and Additional Image Formats
Pierre Chambon, Jean-Benoit Delbrouck, Thomas Sounack +6
Since the release of the original CheXpert paper five years ago, CheXpert has become one of the most widely used and cited clinical AI datasets. The emergence of vision language mo…
INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis
Shih-Cheng Huang, Zepeng Huo, Ethan Steinberg +6
Synthesizing information from multiple data sources plays a crucial role in the practice of modern medicine. Current applications of artificial intelligence in medicine often focus…
LOVM: Language-Only Vision Model Selection
Orr Zohar, Shih-Cheng Huang, Kuan-Chieh Wang +1
Pre-trained multi-modal vision-language models (VLMs) are becoming increasingly popular due to their exceptional performance on downstream vision applications, particularly in the…
Video Pretraining Advances 3D Deep Learning on Chest CT Tasks
Alexander Ke, Shih-Cheng Huang, Chloe P O'Connell +3
Pretraining on large natural image classification datasets such as ImageNet has aided model development on data-scarce 2D medical tasks. 3D medical tasks often have much less data…
Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image Segmentation
Yuhui Zhang, Shih-Cheng Huang, Zhengping Zhou +2
Given the prevalence of 3D medical imaging technologies such as MRI and CT that are widely used in diagnosing and treating diverse diseases, 3D segmentation is one of the fundament…
Diagnosing and Rectifying Vision Models using Language
Yuhui Zhang, Jeff Z. HaoChen, Shih-Cheng Huang +3
Recent multi-modal contrastive learning models have demonstrated the ability to learn an embedding space suitable for building strong vision classifiers, by leveraging the rich inf…