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Jun 22 2020 | JAMA Network Open | Posted In: News
Rajpurkar P, Yang J, Dass N, et al. Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open. 2020;3(6):e206653.
Jun 17 2020 | Posted In: News
The AIMI Center in collaboration with Google Cloud, is offering Stanford researchers the opportunity to receive up to $20,000 per year of Google Cloud research credits on any Google Cloud product. This call for proposals aims to stimulate and support research in the field of artificial intelligence in medicine and imaging that distinctively takes advantage of cloud capabilities. Proposals of all sizes will be considered, from initial exploration of cloud computing usability for projects to more advanced-stage projects. Applications are accepted on a rolling basis.
May 18 2020 | Nature Machine Intelligence | Posted In: News
Mukherjee P, Zhou M, Lee E, Schicht A, Balagurunathan Y, Napel S, Gillies R, Wong S, Thieme A, Leung A, Gevaert O. A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets. Nat Mach Intell 2, 274–282 (2020). https://doi.org/10.1038/s42256-020-0173-6
Apr 24 2020 | Scientific Reports | Posted In: News
El Kaffas A, Rubin DL, Kamaya A. Spatial Characterization of Tumor Perfusion Properties from 3D DCE-US Perfusion Maps are Early Predictors of Cancer Treatment Response. Sci Rep 10 6996 (2020). https://doi.org/10.1038/s41598-020-63810-1
Apr 1 2020 | Posted In: News
Stanford Radiology and the Center for Artificial Intelligence in Medicine and Imaging (AIMI) are pleased to announce a call for proposals sponsored by GE Healthcare. Our longstanding partnership with GE has produced smarter imaging devices that provide more consistent, efficient, and detailed diagnostic information.

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