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Apr 16 2019 | Radiology | Posted In: News
LInks: RSNA Press Release Roadmap Article: Part 1 Roadmap Article: Part 2 Abstract:
Apr 12 2019 | Github | Posted In: News
Andrew Ng’s lab and AIMI researchers released a labeled MRI Knee data for the world to use and participate in an open challenge for deep learning! The AIMI Center believes pursuing reproducibility and transparency of scientific results by making data (and models) from published work available for all to validate, iterate, and ultimately improve on will help everyone achieve better healthcare together as a community! Learn more>>  
Jan 21 2019 | AAAI | Posted In: News
Latest medical AI model and largest chest x-ray dataset ever released, thanks to a wonderful Stanford+MIT collaboration. Abstract: Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, Andrew Y. Ng
Dec 11 2018 | Radiology | Posted In: News
Remarkable work led by Greg Zaharchuk, MD, PhD, showing that diagnostic quality amyloid PET images can be generated using 1% of the PET data obtained from PET/MRI with the help of AI was recently published in Radiology.
Nov 20 2018 | Radiology | Posted In: News
Jared A. Dunnmon , Darvin Yi, Curtis P. Langlotz, Christopher Ré, Daniel L. Rubin, Matthew P. Lungren assess the ability of convolutional neural networks (CNNs) to enable high-performance automated binary classification of chest radiographs.