AI seems to be taking the world by storm, and it is easy to use this new technology for either good or bad. Today I am joined by Sérgio Pereira, the VP of AI Research, Oncology Group, at Lunit, a company using AI for good by conquering cancer with machine learning.
You’ll hear about Sérgio’s professional background, Lunit’s missions, how they use AI for cancer screening and treatment planning, and so much more. Sérgio delves into how they read imaging before discussing the differences between supervised, self-supervised, and contrastive learning. Lunit has created an incredible dataset called Ocelot, and he tells us all about its benefits, how they published it, and why publishing a paper while ensuring that quality products are being produced is a challenge. Finally, Sérgio tells us his hopes for the future of Lunit.
Key Points:
Quotes:
“Our mission at Lunit is to conquer cancer through AI.” — Sérgio Pereira
“We don’t have many products at Lunit, that’s a fact, but the ones we have, we believe they are [the] best-in-class.” — Sérgio Pereira
“Mistakes in healthcare can have a very big impact, so we need to be able to show and demonstrate that our products work as we promised.” — Sérgio Pereira
“AI can be used for good and for bad. Let’s make sure we work on the good part.” — Sérgio Pereira
Links:
Sérgio Pereira on Google Scholar
Lunit Inc.
Paper: OCELOT: Overlapped Cell on Tissue Dataset for Histopathology
Dataset: OCELOT
Paper: Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
Resources for Computer Vision Teams:
LinkedIn – Connect with Heather.
Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.
Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.
Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.