Invited speakers

Danna Gurari – Mixing Crowds, Computers, and Experts for Scalable Annotation of Biomedical Images

Abstract

Biomedical researchers are running image-based studies to systematically study fundamental biological processes. The larger goal of this effort is to contribute to discoveries and innovations that, for example, address society’s health care problems or lead to new bio-inspired technology. However, the key bottlenecks for extracting the desired information from images lie in unreliable annotation from algorithms and costly annotation by experts, especially at scale. Given the rise of crowdsourcing, I will discuss how we can utilize online crowds to better annotate biomedical images. I will present research on demarcating objects in images (segmentation), a critical and time-consuming precursor to many downstream applications. I will begin the talk with a detailed analysis of the relative strengths and weaknesses of three different image segmentation approaches: by experts, by crowd workers, and by algorithms. Then, I will describe a hybrid system design for intelligently distributing segmentation efforts between algorithms and crowds. Results show how to efficiently leverage crowd and algorithm efforts in order to optimize cost/quality trade-offs as well as how to produce segmentations comparable to those created by experts.

Bio

Danna Gurari is currently an Assistant Professor at University of Texas at Austin School of Information. She completed a postdoctoral fellowship in the University of Texas at Austin Computer Science department under the supervision of Dr. Kristen Grauman and her PhD at Boston University in the Image and Video Computing group under the supervision of Dr. Margrit Betke. Her research interests span computer vision, human computation/crowdsourcing, medical/biomedical image analysis, and applied machine learning. In 2007-2010, Danna worked at Boulder Imaging building custom, high performance, multi-camera recording and analysis systems for military, industrial, and academic applications. From 2005-2007, she worked at Raytheon developing software for satellite systems. Danna earned her BS in Biomedical Engineering and MS in Computer Science from Washington University in St. Louis in 2005, with her thesis on ultrasound imaging. Danna was awarded the 2017 Honorable Mention Award at CHI, 2015 Researcher Excellence Award from the Boston University computer science department, 2014 Best Paper Award for Innovative Idea at MICCAI IMIC, and 2013 Best Paper Award at WACV.

Tanveer Syeda-Mahmood

To be announced

Emanuele Trucco – Navigating the perilous waters of validation: the case of retinal image analysis

Abstract

To be announced

Bio

Emanuele (Manuel) Trucco, MSc, PhD, FRSA, FIAPR, is the NRP Chair of Computational Vision in Computing, School of Science and Engineering, at the University of Dundee, and an Honorary Clinical Researcher of NHS Tayside.
He has been active since 1984 in computer vision, and since 2002 in medical image analysis. He has published more than 250 refereed papers and 2 textbooks (one of which an international standard with 2,793 citations, Google Scholar 25 Oct 2016). He is director of VAMPIRE (Vessel Assessment and Measurement Platform for Images of the Retina), an international research initiative led by the Universities of Dundee and Edinburgh (T MacGillivray, tech director). VAMPIRE develops software tools for efficient data and image analysis, especially multi-modal retinal images. VAMPIRE has been used in biomarker studies on cardiovascular risk, stroke, dementia, cognitive performance, neurodegenerative diseases, genetics and more, and has grown strong collaborations with clinical departments around the world. International collaborators include UCLA, Harvard, Tufts, A*STAR Singapore, the Chinese Academy of Science (Ningbo), INRIA, Charite’ Berlin, Univ of Padova and many more. Further, recent projects have focused on robotic hydrocolonoscopy and whole-body MR angiographic data. Current industrial collaborations include Toshiba Medical, OPTOS plc, and Epipole retinal cameras. Manuel is particularly interested in validation, the reliability of retinal measurements, and its consequences for statistical inference.