Home Department: Engineering
Mentor: Christina Curtis (Medicine - Oncology, Genetics, and Biomedical
Data Science)

“Efficient Implementation of Novel Methods for Measuring Scalp to Cortex Distance"

This project aims to translate ‘omics insights in breast cancer into the clinic. ‘Omics data have led to better classifications of subtypes and classifications in breast cancer, yet are too resource-intensive to incorporate in the clinic. Darren aims to develop and improve the performance of a machine learning model that takes in biopsy H&E (hematoxylin and eosin) whole slide images and predicts learned prognostic indications from breast cancer genomics. Additionally, Darren aims to increase the model’s interpretability and elucidate the biological factors that contribute to prognosis prediction—allowing for researchers and pathologists to better understand and verify the findings.