StoneAnno is my first published first-authorship paper, presenting at SPIE 2022. With the long-term goal of fully-automated robotic endoscopic surgery, we built a dataset of endoscopic kidney stone removal videos and investigated U-Net, U-Net++, and DenseNet for the segmentation task. We found a U-Net++ model that consistently achieves >0.9 Dice score, with low loss, and produces realistic, convincing segmentations. Moving forward, I am implementing our model on hardware for deployment in ORs, as a part of my master’s thesis, and I helped Dr. Kavoussi submit an R21 grant in October 2021.
Although it will be published after
StoneAnno, this shape analysis is my first completed research project and technically my first first-authorship,
submitted to Brain. I wrote code in R and MATLAB to fit LMMs to the cortical data from T1w MRI of HD patients and then performed statistical analyses on the results using SurfStat and random field theory. We found that, with a novel method for measuring gyrification,
LGI uniquely detects changes in the insula.Neural network automated verification of a VAE and SegNet using NNV. Although neural networks are promising, they are easily confused, particularly if the input domain is perturbed. In this project, I demonstrate the robustness of MNIST-trained VAE and SegNet against varying brightness attacks.
I fell in love with dimensionality reduction when I was learning statistical ML. Since I also study neuroscience, I wanted to practice the art at the intersection of my interests. I compared the 3D projections of a 53-dimensional neurophysiology dataset produced by PCA and a shallow autoencoder.