On August 9 and 10, I participated in the UAB AI Against Cancer Data Science Hackathon. My teammates and I applied recent advances in Computer Vision Artificial Intelligence applied to pathology slides (such as tissue samples of brain cancer). We were one of the three teams receiving the “main awards” of the hackathon. The three winning teams were selected on criteria similar to that of NIH grant-proposal review (see details at Hackathon 2021 – Cancer Bioinformatics and Data Science (C-BIDS) (ubrite.org)). You can view our showcase presentation of our work here on YouTube.
My teammates were Thi K. Tran-Nguyen and Tarun Karthik Kumar Mamidi, along with Liz Worthey and Rati Chkheidze (these two proposed the project focus). Together, our skills covered the gamut of data wrangling, model development, and medical relevance. As a result, we developed a proof of concept system for combining pathology slides and omics data in order to select cellular pathways based on omics data and have the AI highlight the participating cells on the slide (Figure below).
We set a particularly difficult challenge for ourselves, as we wanted to develop an unsupervised methodology. That is, we wanted an AI to accomplish this task without expert input or intervention. We did not provide examples of “right” answers for the computer to learn from! Overall, our short time in the hackathon proved feasibility for a method for connecting genotype (from omics data)
