most citedDDM: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models

17 citations · 26 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG20246 cited

Deep Learning for Accelerated and Robust MRI Reconstruction: a Review

Reinhard Heckel, Mathews Jacob, Akshay Chaudhari +2

Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides…

cs.CV2023

ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data

Maya Varma, Jean-Benoit Delbrouck, Sarah Hooper +2

Vision-language models (VLMs), such as CLIP and ALIGN, are generally trained on datasets consisting of image-caption pairs obtained from the web. However, real-world multimodal dat…

eess.IV2023

The Effect of Counterfactuals on Reading Chest X-rays

Joseph Paul Cohen, Rupert Brooks, Sovann En +4

This study evaluates the effect of counterfactual explanations on the interpretation of chest X-rays. We conduct a reader study with two radiologists assessing 240 chest X-ray pred…

cs.CV20239 cited

Comp2Comp: Open-Source Body Composition Assessment on Computed Tomography

Louis Blankemeier, Arjun Desai, Juan Manuel Zambrano Chaves +11

Computed tomography (CT) is routinely used in clinical practice to evaluate a wide variety of medical conditions. While CT scans provide diagnoses, they also offer the ability to e…

eess.IV202317 cited

DDM: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models

Tiange Xiang, Mahmut Yurt, Ali B Syed +2

Magnetic resonance imaging (MRI) is a common and life-saving medical imaging technique. However, acquiring high signal-to-noise ratio MRI scans requires long scan times, resulting…