Research Project Title:
Profiling Cell Interactions in Tissue Images Using Multi-Modal Machine Learning.
abstract:For fields such as cancer treatment, it is crucial for us to understand how genetic alterations are reflected in tissue histology. A complete understanding of both is critical for tasks such as assessing patient risk and predicting survival outcomes. My project intends to build upon our current knowledge of genotype-phenotype associations by predicting gene expression signatures using histopathological features, and vice versa. I will leverage a deep learning approach: vision transformers with self-supervised learning to extract important features from tissue images. I also plan to integrate multi-modal data available through The Cancer Genome Atlas project to gain a cohesive genotype-phenotype understanding.
Through this SuperUROP project, I hope to apply my machine learning knowledge (particularly from 6.864 and 6.869) to an advanced research project. I’m very excited to explore deep learning’s applications in the medical/biological fields and to continue to hone my technical skills, while making a meaningful contribution to my group. Ultimately, I am hopeful that this project may evolve into (or at least be good experience for) a future MEng thesis.