Image-based Morphological Profiling Using Deep Learning
Microscopy images are widely used in biological research to understand cellular structure. Images can be used to measure multiple properties of cells, and then model the effect that treatments produce at the cellular level. These measurements can be used to discover novel relationships between treatments. We investigate whether relevant cellular features can be learned automatically from images using deep learning. In this talk, we present the results of our experiments, which indicate that deep learning features can improve the ability to identify unknown biological relationships.
Juan Caicedo is a postdoctoral researcher at the Broad Institute of MIT and Harvard, where he investigates the use of deep learning to analyze microscopy images. Previous to this, he studied object detection problems in large scale image collections also using deep learning, at the University of Illinois in Urbana-Champaign. Juan completed research internships in Google Research, Microsoft Research, and Queen Mary University of London in the past, studying problems related to large scale image classification, image enhancement, and medical image analysis. His research interest include computer vision, machine learning and computational biology.