Nick Heller - Machine Learning for Medical Image Analysis

Super excited the share the conversation I had with Nicholas Heller. Nick is PhD student in the Department of Computer Science and Engineering at the University of Minnesota -- Twin Cities.

Nick's research focuses on the development and validation of clinical prediction models for risk stratification and treatment planning in genitourinary cancer, especially renal cell carcinoma. In particular, he's interested in the use of deep learning to incorporate tumor appearance into prediction models in more expressive and objective ways while maintaining transparency and biological plausibility.

Nick shares some really good books that I have listed in the notes below. Please reach out to Nick on his personal UMN page.

If you are interested in learning about how AI is being applied across multiple industries, be sure to join us at a future Applied AI Monthly meetup and help support us so we can make future Emerging Technologies North non-profit events! 

• Emerging Technologies North
• AppliedAI Meetup

Resources and Topics Mentioned in this Episode

• Medical Imaging
• Classification
• Segmentation
• Amazon Mechanical Turk
• Bias and Unbiased
• BraTS Dataset
• BraTS on Kaggle
• KiTS Challenge
• Nick’s Github Repository
• Nick’s KiTS Data
• Transfer Learning
• Cross-Sectional Imaging
• 510(K) Clearance
• Andrew Ng
• Michael Neilson
• Pattern Recognition and Machine Learning
• Intro to Statistical Learning
• Deep Medicine

Enjoy!
Your host,
Justin Grammens