13 citations · 20 across the 7 of their papers we have counts for
7 papers
DeepCORO-CLIP: A Multi-View Foundation Model for Comprehensive Coronary Angiography Video-Text Analysis and External Validation
Sarra Harrabi, Yichen Wu, Geoffrey H. Tison +6
Coronary angiography is the reference standard for evaluating coronary artery disease, yet visual interpretation remains variable between readers. Existing artificial intelligence…
Automated Interpretable 2D Video Extraction from 3D Echocardiography
Milos Vukadinovic, Hirotaka Ieki, Yuki Sahashi +2
Although the heart has complex three-dimensional (3D) anatomy, conventional medical imaging with cardiac ultrasound relies on a series of 2D videos showing individual cardiac struc…
Medical Image De-Identification Benchmark Challenge
Linmin Pei, Granger Sutton, Michael Rutherford +67
The de-identification (deID) of protected health information (PHI) and personally identifiable information (PII) is a fundamental requirement for sharing medical images, particular…
EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound Noise
David Choi, Milos Vukadinovic, Bryan He +3
Echocardiography (echo), or cardiac ultrasound, is the most widely used imaging modality for cardiac form and function due to its relatively low cost, rapid acquisition time, and n…
EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation
Milos Vukadinovic, Xiu Tang, Neal Yuan +5
Echocardiography is the most widely used cardiac imaging modality, capturing ultrasound video data to assess cardiac structure and function. Artificial intelligence (AI) in echocar…
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
Hyewon Jeong, Sarah Jabbour, Yuzhe Yang +40
The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between…
Multimodal Foundation Models For Echocardiogram Interpretation
Matthew Christensen, Milos Vukadinovic, Neal Yuan +1
Multimodal deep learning foundation models can learn the relationship between images and text. In the context of medical imaging, mapping images to language concepts reflects the c…