EyeSpy AI: Retinal Video Sequences
- For our entry in UC Berkeley’s 5th Annual Datathon, I, along with my three teammates Jonathan Ferrari, Dhruv Pendharkar, and Miles Wang, crafted a Variational Autoencoder capable of producing videos from retinal scans and their numerical frameshift sequences. We earned an Honorable Mention in the competition.
- The link to our presentation can be found here!
Description
- The GitHub Repo for this project can be found here!
- Coded a Variational Auto-Encoder (VAE) neural network to convert retinal movement numerical data into generative AI video simulations of retinal movement afflicted by one of three different diseases
- Implemented a data preprocessing pipeline that converts .avi files into sets of 300 individual frames
- Ran training data through a Long-Short Term Memory (LSTM) network to encode data into latent space, then decoded data using a Gated Recurrent Unit (GRU), producing video simulations in 512x512 resolution
Skills
- Computer Vision
- Machine Learning
- Deep Learning