Current Challenges in Deep Learning for Medical Image Interpretation
Deep Learning models can be used to diagnose melanoma, breast cancer lymph node metastases and diabetic retinopathy from medical images with comparable accuracy to human experts. This talk covers work in applying deep learning to imaging for diabetic retinopathy, cancer screening & diagnosis, including recent work in using different reference standards and techniques to improve explainability. It will also cover how deep learning can be leveraged to make novel predictions such as cardiovascular risk factors and disease progression
3 Key Takeaways:
*'More data' is not sufficient to generate a better model
*Accurate models are not enough to create a useful product
*A good product is not sufficient to realize clinical impact for patients
Dale Webster is Director of Research at Google Health working to improve patient outcomes in healthcare using Deep Learning and Medical Imaging. His recent work leverages AI to screen for Diabetic Retinopathy in India and Thailand, predict Cardiovascular health factors from fundus photos, and differential diagnosis of skin disease. Prior to Google he was a Software Engineer at Pacific Biosciences working on direct sequencing of methylation state and rapid sequencing and assembly of microbial pathogens during global outbreaks. His PhD work in Bioinformatics at the University of California San Francisco focused on viral evolution, and he received his Bachelor of Science in Computer Science from Rice University.