32 citations · 86 across the 81 of their papers we have counts for
15 papers · 1 filter
Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance
Panagiotis Fytas, Ian Selby, Clemens Karner +14
Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment. However, commonly used report-derived…
Neural Fields for Highly Accelerated 2D Cine Phase Contrast MRI
Pablo Arratia, Martin J. Graves, Mary McLean +5
2D cine phase contrast (CPC) MRI provides quantitative information on blood velocity and flow within the human vasculature. However, data acquisition is time-consuming, motivating…
Implicit U-KAN2.0: Dynamic, Efficient and Interpretable Medical Image Segmentation
Chun-Wun Cheng, Yining Zhao, Yanqi Cheng +3
Image segmentation is a fundamental task in both image analysis and medical applications. State-of-the-art methods predominantly rely on encoder-decoder architectures with a U-shap…
Benchmarking learned algorithms for computed tomography image reconstruction tasks
Maximilian B. Kiss, Ander Biguri, Zakhar Shumaylov +4
Computed tomography (CT) is a widely used non-invasive diagnostic method in various fields, and recent advances in deep learning have led to significant progress in CT image recons…
Parameter choices in HaarPSI for IQA with medical images
Clemens Karner, Janek Gröhl, Ian Selby +11
When developing machine learning models, image quality assessment (IQA) measures are a crucial component for the evaluation of obtained output images. However, commonly used full-r…
Learned denoising with simulated and experimental low-dose CT data
Maximilian B. Kiss, Ander Biguri, Carola-Bibiane Schönlieb +2
Like in many other research fields, recent developments in computational imaging have focused on developing machine learning (ML) approaches to tackle its main challenges. To impro…