Artificial intelligence for medical images
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.