CLSSS: Machine Learning Implementation of Cancer Histology Actionable Gene Detection

Andrew Srisuwananukorn, an Internal Medicine resident at UIC, presents on the use of convolutional neural networks and machine learning at large in medicine. He will highlight his work with UC Medical Oncologist Alex Pearson on assessing histology slides to distinguish actionable subsets of cancer, detect microsatellite instability in colon cancer, and HPV-status in Head and Neck cancer. Finally, he presents ongoing efforts to detect actionable gene mutations in the pan-cancer TCGA dataset.