Advaith Veturi's Portflio
When you Google the terms “Deep learning and Medicine” or “Machine learning and medicine”, most of the articles you see will probably be about its uses in radiology-based diagnosis, electronic healthcare records, basically the clinical data. Over the past couple of years, there has been increasing ML research on the more cellular level, focusing on … Continue reading Zooming into the cells – Genomics and Bioinformatics
Recently, I submitted my thesis project, which was a culmination of my Master’s course in Machine Learning at UCL. This is my second major project combining AI with medical imaging, and I had a great experience collaborating with my supervisor, Dr. Nikolas Pontikos, and everyone at Pontikos Lab. Specifically, my project focused on Generative Adversarial … Continue reading SynthEye
Generative modeling and GANs are popular in the media today because of their success in various tasks like generating deep fakes, digitally painting realistic portraits, converting images from one style to another, denoising images, etc. While these are really cool innovations, I think the translation of these ideas into the industry and our daily lives … Continue reading 3 Ways GANs can Change Medicine
Previously, I discussed in-depth the vanishing gradient problem in GANs and how it leads to unstable and ineffective learning. I then introduced the Wasserstein GAN, one of the popular solutions to this problem. While most research efforts into GANs have been in line with this goal of improving training stability, the game doesn’t end here. There … Continue reading Improving the GAN Part 2: Conditional and Controllable generation
In my previous post, I gave an introduction to generative adversarial networks (GANs), discussed the basic training algorithm, and implemented a basic GAN for generating handwritten digits. While this basic model works well and has been shown to produce decent images, there are many difficulties that one can encounter during training, a popular one being … Continue reading Improving the GAN
A couple of months back, I found this fantastic Medium article by Susan Li, which implemented a Bayesian model for estimating the effects of COVID-19 vaccines. As I had studied these topics as part of my course on Graphical models, I thought it would be interesting to re-implement this myself from scratch and break down … Continue reading Bayesian inference, MCMC and comparing COVID vaccines
You must be wondering who these random people in the above picture are. What if I told you they don’t exist and were created by a computer? I was pretty amazed when I saw this at an AI talk I attended a couple of years back, and it still amazes me to this day. These … Continue reading Introduction to Generative Adversarial Networks
I’m sure most of us will know that proteins play a huge role in the human body. They are responsible for the metabolic reactions in our cells, carry molecules from one part of the body to another, mediate cellular repair, and form a part of our immune system. But to figure out what a protein’s … Continue reading Predicting the sub-cellular location of a protein using machine learning
On 16th April 2021, I gave a talk on Generative Adversarial Networks or GANs, a hotspot in deep learning research. For my master’s thesis, I am investigating how this can be used in medical image synthesis, specifically retinal image synthesis for generating image augmentations to improving model training. Do watch my talk to learn more … Continue reading Intro to Generative Adversarial Networks
As part of my biomedical imaging module, I participated in a group project focused on studying the connectome using graph theory and parametric models. In this post, I’ll briefly overview connectomics, how connectomes are constructed, our team’s experiments, and my big takeaways from the project. What is connectomics? If you’ve read a lot about biological … Continue reading Modelling the Relationship Between Structural and Functional Connectomes
