Impact AI Detecting Breast Cancer Earlier with Tobias Rijken from Kheiron Medical
All women face the risk of breast cancer, but early detection can greatly increase the chances of a positive outcome and reduce the need for aggressive treatment options. In this episode, I talk with Tobias Rijken, CTO and co-founder of Kheiron Medical Technologies, about leveraging AI for detecting breast cancer. We discuss the role of AI in improving medical care, the power of vertical integration and feedback loops, and what makes Kheiron different from other AI startups. Hear about the challenges of acquiring reliable data, whether using generative models is beneficial, details about the products Kheiron has created, and much more!
Key Points:
- Tobias's professional background and why he created Kheiron Medical Technologies.
- Learn about the amazing work Kheiron Medical Technologies does and why it is important.
- Overview of why detecting breast cancer early is so vital and the challenges of screening.
- How AI can help resolve the current challenges in cancer screening.
- He explains the machine learning process and training the model used.
- The complications encountered in working with radiology images.
- Find out why image quality is key to the machine learning process.
- How he is able to account for the variation of technology and methods used.
- Outline of the regulatory process and how it impacts machine learning model development.
- Hear advice Tobias has for other leaders of AI-powered startups.
- Details about how Tobias approaches improving the models over time.
- Tobias tells us what Kheiron Medical Technologies has planned for the future.
Quotes:
“What I liked so much about machine learning is the ability it has to solve real-world problems. And in my opinion, real-world machine learning is very different from academic machine learning.”
“Either the right information isn't available, or it is inaccurate, or there's missing information. We see AI as a tool to help address those information problems.”
“The challenge when you sample uniformly from your whole dataset is that there will be cases you've sampled, where you may not have ground truth.”
“For me, when I started this company, this was not about building a great model that has a great performance on a test dataset. This is about getting AI into the real world.”
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