University of Pittsburgh

Breast Cancer Risk Assessment via Symmetrical and Temporal Changes

graduate student
Friday, November 22, 2019 - 1:00pm - 1:30pm


On average, nowadays, every two minutes a woman is diagnosed with breast cancer in the United States. Identifying significant risk factors in developing breast cancer has been an active research area for a long time and can help to prevent or in early detection.  Mammograms as an efficient way allow physicians to diagnose any suspicious fibroglandular tissue and if necessary, ask for the next screening step such as biopsy. Developing asymmetry is a term that recently has caught the attention of physicians for diagnosing malignant tissues. By considering that, they can put current and previous mammograms together and locate any abnormality in tissue. Although, there are some studies in using prior mammograms to predict the cancer case 1 to 5 years beforehand; However, no specific investigation has been done in discovering any association between developing asymmetry and breast cancer risk prediction. Inspiring by how radiologists use prior mammograms along with advances in video temporal analysis, we propose an end-to-end deep learning method to investigate this relationship. Our structure is designed to calculate the difference between left and right breasts in prior mammograms and subsequently find the temporal correlation between them to assess the risk of cancer in the specific patient.  Final results comparing previous risk assessment methods demonstrate the existence of an association between developing asymmetry and normal vs cancerous patients. 

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