The role of AI in cancer detection grows more significant with each passing week. During this conversation, I welcome Marcel Gehrung, CEO and Co-Founder of Cyted, to discuss detecting gastrointestinal cancer. You’ll learn how Cyted leverages machine learning to diagnose Barrett’s Esophagus in upper GI samples. Marcel reveals some of the challenges he has faced at Cyted related to the limited autonomy an algorithm can realistically provide, and annotating data for training and validation. Hear how the company is responding to changes in AI, and why hiring for technical roles at Cyted has not been difficult, due to their location. You’ll hear Marcel’s perspective on hiring specialist generalists and some of his advice for leaders at AI-powered startups.
Key Points:
Quotes:
“We’re essentially leveraging the best of both worlds. We’re working with cytoscreeners, which we also have on our staff to generate the initial annotations, and then we have someone who looks at it and then reclassifies if necessary.” — Marcel Gehrung
“The more ability the candidates have to horizontally integrate different types of knowledge from across the company or across the technology of the sector, the better.” — Marcel Gehrung
“Getting carried away just happens so easily, particularly when we follow the various news outlets in the world that overwhelm us with new exciting ideas and functions of that technology.” — Marcel Gehrung
Links:
Marcel Gehrung on LinkedIn
Marcel Gehrung on Twitter
Cyted
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.