Machine Learning for Precise and Equitable Cancer Care.

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Our 

Mission

Cancer inflicts a heavy toll on our society. One out of seven women will be diagnosed with breast cancer during their lifetime, a fraction of them contributing to about 450,000 deaths annually worldwide. Despite billions of dollars invested in cancer research, our understanding of the disease, treatment, and prevention is still limited. Majority of cancer research today takes place in biology and medicine. Computer science plays a minor supporting role in this process if at all. We firmly believe that recent advances in machine learning, natural language processing and computer vision can revolutionize cancer care. Data collected about millions of cancer patients -- their pathology slides, imaging, and other tests -- contain answers to many open questions in oncology. We are developing algorithms that can learn from this data to improve models of disease progression, prevent over-treatment, and narrow down to the cure. All of our algorithms are publically available to the research community. 

 

Announcements

Robust AI tools to predict future breast cancer
We created a risk assessment algorithm that shows consistent performance across datasets from the US, Europe and Asia.

News

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January 27, 2021

Robust AI tools to

predict future

cancer

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November 19, 2020

A leading AI researcher calls for standards to ensure equity and fairness

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Publications

A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction
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Adam Yala , Constance Lehman, Tal Schuster, Tally Portnoi, Regina Barzilay
RSNA Radiology 2019.
*Top 10 RSNA Radiology papers by Downloads 2018. Top 10 RSNA Radiology papers by Altmetric 2018. 
A Deep Learning Model to Triage Screening Mammograms:
A Simulation Study
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Adam Yala, Tal Schuster, Randy Miles, Regina Barzilay, Constance Lehman
RSNA Radiology 2019.
Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
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Constance D. Lehman , Adam Yala, Tal Schuster, Brian Dontchos, Manisha Bahl, Kyle Swanson, Regina Barzilay
RSNA Radiology 2018. *Top 10 RSNA Radiology 2018.
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Our Team

Regina

Barzilay

Adam

Yala

Jeremy Wohlwend

Peter

Mikhael

Justin

Xiang

Clinical Partners

Kevin Hughes

Connie Lehman

Fredrik Strand

Gigin

Lin