Advances in optics, biological sensing, medical imaging technologies, high throughput genetic sequencing is leading to massive datasets, which need to be analyzed. However, current Artificial Intelligence algorithms usually require 1000’s of examples of well-annotated datasets for high accuracy classification. Fluorescent biomarkers are important indicators of disease such as oral cancer, but imaging them can require specialized and often-expensive devices. Medical images, if diagnosed early with biomarker images and expert knowledge, can be valuable to prevent occurrences of serious systemic illnesses. In this lecture, we will discuss two convolutional neural network classifiers trained with disease signatures and fluorescent biomarker images to identify biomarkers in white light images as a per-pixel binary classification task. Once trained, the classifiers predict the location and intensity of fluorescent biomarkers in white light images without requiring specialized biomarker imaging devices or expert intervention. This generalized approach can be useful in other domains where diagnostic biomarker predicting can augment expert knowledge using standard white light images.
Manufacturing Work of the Future: Technology, Institutions, and Possibilities
2020 Future of Manufacturing - Elisabeth B Reynolds
Tricia Dinkel Manager of Partnerships & Engagement, MIT Startup Exchange
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Digital Health mobile apps and connected medical devices are rapidly changing how patients learn, monitor, diagnose and treat disease. Even in these early days of the digital transformation of healthcare, connected medical devices and digital services are winning reimbursement as “digiceuticals” by payors and insurers. However, the critical need going forward is how to measure, compare and prove these new tools and digital biomarkers are safe, effective and valuable at scale, not just in the USA but globally, across geographies, cultures and health systems.
Single-cell mass as a clinical tool for guiding treatment decisions
Keith Ligon Associate Professor of Pathology at Harvard Medical School Scott Manalis Andrew and Erna Viterbi Professor of Biological Engineering Clifford Reid CEO of Travera
Polina Golland will discuss her group's research in computational analysis of MRI scans that aims to provide accurate measurements of healthy anatomy and physiology, and biomarkers of pathology. Applications range from fetal development to aging brain.