Séminaire du laboratoire du 18 octobre à 14h en salle 018 (RDC du bâtiment Pascal, Faculté des Sciences et Technologies).
L’orateur est Juan C. Caceido du Broad Institute du MIT et Harvard.
Sa présentation portera sur la vision par ordinateur pour la biologie quantitative.
La biographie et l’abstract sont ci-dessous.
Short Bio : Juan Carlos Caicedo is an associate 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 obtained a PhD from the National University of Colombia doing research in multimodal information retrieval. He completed research internships in Google Research, Microsoft Research, and Queen Mary University of London as a grad student, working in problems related to large scale image classification, image enhancement, and medical image analysis. His research interest include computer vision, machine learning, information retrieval and computational biology.
Title : Computer Vision for Quantitative Biology
Abstract : Important progress has been made with the application of convolutional neural networks (CNNs) to computer vision problems. One of the defining characteristics of a solution based on CNNs is the amount and quality of annotated data needed to train and validate such models. As a result, the vision community is very active in creating, labeling and organizing large collections of internet images and videos. But what happens in other application domains where creating manually annotated datasets can be very expensive ? Even more, what if there is no such a thing as ground truth, because images are used to discover something that we don’t know yet ? This is the case in basic biological research, where annotations cannot be crowdsourced, and the knowledge contained of images is still to be understood. In this talk, I will present the machine learning strategies that we are developing to overcome these issues to study cancer biology and support the drug discovery process at large scale.