86 citations · 190 across the 11 of their papers we have counts for
10 papers · 1 filter
X-WIN: Building Chest Radiograph World Model via Predictive Sensing
Zefan Yang, Ge Wang, James Hendler +2
Chest X-ray radiography (CXR) is an essential medical imaging technique for disease diagnosis. However, as 2D projectional images, CXRs are limited by structural superposition and…
Explaining Chest X-ray Pathology Models using Textual Concepts
Vijay Sadashivaiah, Pingkun Yan, James A. Hendler
Deep learning models have revolutionized medical imaging and diagnostics, yet their opaque nature poses challenges for clinical adoption and trust. Amongst approaches to improve mo…
Disease-informed Adaptation of Vision-Language Models
Jiajin Zhang, Ge Wang, Mannudeep K. Kalra +1
In medical image analysis, the expertise scarcity and the high cost of data annotation limits the development of large artificial intelligence models. This paper investigates the p…
Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment
Florin C. Ghesu, Bogdan Georgescu, Eli Gibson +6
The interpretation of chest radiographs is an essential task for the detection of thoracic diseases and abnormalities. However, it is a challenging problem with high inter-rater va…
Knowledge-based Analysis for Mortality Prediction from CT Images
Hengtao Guo, Uwe Kruger, Ge Wang +2
Recent studies have highlighted the high correlation between cardiovascular diseases (CVD) and lung cancer, and both are associated with significant morbidity and mortality. Low-Do…
Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?
Hongming Shan, Atul Padole, Fatemeh Homayounieh +5
Commercial iterative reconstruction techniques on modern CT scanners target radiation dose reduction but there are lingering concerns over their impact on image appearance and low…