Machine Learning and Medical Imaging: The Future of Early Cancer Detection – FOSSASIA 2017

Speaker(s): Gaeun Kim (Stanford, California)

Abstract:
The Translational Molecular Imaging Lab at the Stanford Canary Center for Cancer Early Detection applies machine learning algorithms to extensive collections of ultrasound data to improve pancreatic and breast cancer diagnostics. I will present an overview of the MeVisLab software that we use to analyze and segment 3D ultrasound images, and then dive into the machine learning techniques that are used to improve spatial and temporal resolution for the detection of molecularly targeted ultrasound contrast agents. These techniques, applied to big data and combined with in vitro blood testing, have significantly improved the accuracy of cancer diagnostics, and present the future of early cancer detection. As a concluding remark, I will discuss the potential of open-sourcing medical imaging data and the benefits it will bring to the medical field as a whole, telling my story of how I originally got involved with an open source project called CancerBase.org and how that developed into an opportunity to work at a professional lab.

(Type: Talk | Track: AI & Machine Learning | Room: Mendel (Ground Floor))

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