Computational Imaging and Analysis in Breast Cancer

Justin Lee, Massachusetts Institute of Technology

The overall objective of my projects has been to accurately predict recurrence risk in breast cancer patients from pathology slides using computational imaging and machine learning. Knowledge of a patient’s risk of recurrence is important, as it helps determine what treatment they should or should not receive. For instance, a patient may have a prognosis so favorable that the benefit of chemotherapy is likely to be very low. In such a case, the patient should probably not receive chemotherapy.

In this talk, I will discuss: computational imaging techniques, such as Fourier ptychography, used for enhanced image acquisition; machine-learning algorithms applied to breast cancer pathology images in order to predict patient recurrence risk; and an end-to-end system under development to aid pathologists in clinical decision-making.

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