MIT EECS Angle Undergraduate Research and Innovation Scholar
Representations of Leukoaraiosis in Clinical Brain Images
Electrical Engineering and Computer Science
- Machine Learning
Representations of White Matter Hyperintensity in Clinical Brain Images
Analyzing the severity of cerebrovascular disease such as ischemic stroke or leukaraiosis is important in stroke prognosis and treatment. Cerebrovascular disease presents with high variability in shape and location in the brain making it difficult to identify patterns or make predictions. Leukaraiosis or non-specific vascular disease observable on medical images has a more characteristic behavior hyperintense on T2-FLAIR imaging often peri-ventricular and roughly bilaterally symmetric. As such I will explore mathematical representations to capture leukaraiosis in brain images. Such representations will help in pathology segmentation and stroke prognosis offering a better understanding of cerebrovascular disease.
I have been in Polina Golland’s group for a year and a half and I love my work here. My UROP project in this lab has been an incredible learning experience; as such I want to take my research path to the next level by leading a new project through the SuperUROP program. This opportunity will expand my knowledge in machine learning research and connect me with other professors from areas related to my research.