Marcelo Zibetti, imaging scientist at NYU Langone Health, talks about efficiency in MRI, the value of differing vantage points, and learning by thinking across disciplines.
Tag: Artificial Intelligence
Yiqiu “Artie” Shen, machine learning researcher who develops artificial intelligence systems for medical imaging, talks about AI’s ability to explain itself, guide discovery, and predict cancer risk.
Radhika Tibrewala, graduate student in biomedical imaging, talks about the new fastMRI prostate dataset, deep learning in MRI, and how she started a PhD remotely in 2020.
Patricia Johnson, who researches machine learning image reconstruction, talks about faster MRI, visual preferences, and diagnostic interchangeability.
Krzysztof Geras and Jan Witowski, machine learning researchers in medical imaging, talk about understanding one’s data, working across disciplines, and radiologists’ “new colleague.”
A combination of radiologists and AI reduced overdiagnosis and more accurately identified breast cancer in ultrasound exams.
Florian Knoll, incoming chair of imaging at University of Erlangen–Nuremberg, talks about his background, not being greedy, and why he does what he does.
NYU researchers won a deep learning challenge to detect lesions in digital breast tomosynthesis (DBT) images. We discuss the obstacles that DBT poses to deep learning and look at how our team navigated them.
NIH-funded deep learning research at NYU Grossman School of Medicine promises to predict progression of knee osteoarthritis.