Autumn 2021-2022 Projects

• Rapid segmentation of the central lateral thalamic nucleus
Matthew Radovan
• Transformer for Prediction of Patient Trajectories from Electronic Health Records
David Huang
• Trans-UNet for Cell Segmentation
Michael Zhang
• Predicting Unit-Level Transfers at Lucile Packard Children’s Hospital
Harry Koos, Hannah Li, Meredith Xu
• Applying Transformer-based Sequence Modeling to ECG Data
Thomas Jiang, Claire Mai, Avanika Narayan
• Predicting Cell Type from Spatial Transcriptomic Data
Ayushi Tandel, Nathaniel Chien
• Explainable Deep Learning Application to Predict Key Adverse Outcomes After Injury
Jeff Choi, Shobha Dasari, Allen Huang
• CheXT - The Chest X-ray Transformer, for Deep Learning based Free-Text Annotation of Medical Imaging Scans
Henry Mellsop, Colton Swingle
• Predicting Left Ventricular Ejection Fraction Using Cardiac Echocardiograms
Katelyn Bechler, Raghav Garg, Vy Ho
• Deep Learning for Early Prediction of Septic Shock Treatment in Emergency Department Patients using Physiologic Monitor Data
Matthew Kolodner
• Dealing with Confounder Variables in Deep Learning Models
Navami Jain
• Classification of Brain Tumor Radiogenomics
Eden Grown-Haeberli, Emma Mclean
• Predicting CRISPRCas9 sgRNA On-target Efficacy with Deep Learning
Sam Spinner
• Sequence-Based Deep Learning DNA Methylation Prediction and Whole-Genome Epigenetic Aging Prediction
Sophie Parsa, Marc Huo
• OCT-GAN: Conditional Generation of Optical Coherence Tomography Images to Aid Retinal Disease Classification
Bryan Gopal, Brian Soetikno
• Learning protein sequence embeddings for alignment free sequence comparison
Sophia C. Kivelson

Note: some reports are not listed at the request of those students.