Transforming Medicine
with Trustworthy AI.

We innovate AI for better medical care and patient outcomes.

MRIMath’s mission is to improve cancer diagnosis, treatment response, and prognosis. We bring expertise in medical imaging with trustworthy and explainable artificial intelligence to empower physicians with reliable, cost-effective and interactive human-AI interface. MRIMath solutions redefine treatment planning, diagnosis, surveillance, and radicomics in oncology.
MRIMath is the medical imaging AI platform allowing you to weave leading AI clinical applications directly into your existing PACS or EHR driven workflow to make it a natural extension of what you already do.
We are making AI real by improving physician experience, accuracy of diagnosis and treatment, financial performance and outcomes that matter to patients and providers.
Accessible anywhere from any validated device via the cloud for faster performance, ease of deployment with no PHI exchange and completely secure.

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Our Funding

Publications

Hassan M. Fathallah-Shaykh, Andrew DeAtkine, Elizabeth Coffee, Elias Khayat, Asim Bag, Xiaosi Han, Paula Province Warren, Markus Bredel, John Fiveash, James Markert, Nidhal Bouaynaya and Louis B. Nabors, “Diagnosing growth in low-grade gliomas with and without longitudinal volume measurements: A retrospective observational study ,” PLOS Medicine, vol. 16, no. 5, May 2019.

Daniel E. Cahall, Ghulam Rasool, Nidhal C. Bouaynaya and Hassan M. Fathallah-Shaykh, “Inception Modules Enhance Brain Tumor Segmentation,” Frontiers in Computational Neuroscience, July 2019.

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS Challenge. Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Fathallah-Shaykh, HM, et al. arXiv:1811.02629v2. 2019 Mar 19.

Palumbo, O, Dimah, D, Bouaynaya, Fathallah-Shaykh, HM Inverted Cone Convolutional Neural Network for Deboning MRIs. International Joint Conference of Neural Networks, 2018, Buenos Aires, Brazil. 2018.

Guo, J, Liang, Z, Scribner, E, Ditzler, G, Bouaynaya, N, Fathallah-Shaykh, HM. Nonlinear Brain Tumor Model Estimation with Long Short-Term Memory Neural Networks. International Joint Conference of Neural Networks, 2018, Buenos Aires, Brazil.