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Machine Learning for Mammography

Screening mammography saves lives, yet the volume of cases generated by screening puts an enormous demand on radiologists. We are using deep learning to create tools that will help radiologists interpret mammograms more accurately and efficiently, for both 2D full-field digital mammograms and 3D digital breast tomosynthesis.

Algorithms Based on Expert Knowledge

Our models learn from interpretations provided by expert radiologists and pathologists to map images to gold standard diagnoses.

Learning from Lifetimes of Data

Our models have been trained on more data than a single clinician could possibly see in her/his lifetime.

Improving Patient Outcomes

Ultimately, our greatest goal is to improve the lives of patients, by helping detect cancers as early as possible while minimizing unnecessary callbacks.


Reading Mammograms is Difficult

The subtle nature of cancerous lesions and the large number of cases to interpret can be straining, especially for digital breast tomosynthesis.

Beyond Traditional CAD

Our software uses deep learning to achieve higher performance levels than traditional CAD, enabling more effective clinical use cases.

An Accuracy Boost

Our software highlights a small number of suspicious lesions so breast imagers can identify more cancers without having to sort through all of the false positives generated by traditional CAD.

Software as an Assistant

Our software empowers radiologists by helping them interpret more efficiently, so they can spend more time on other critical tasks.

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