Invited speakers

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


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.



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 – Challenges of large-scale data annotations for building cognitive medical assistants

Abstract to be announced

Dr. Tanveer Syeda-Mahmood is an IBM Fellow and Chief Scientist/overall lead for the Medical Sieve Radiology Grand Challenge project in IBM Research, Almaden. Medical Sieve is an exploratory research project with global participation from many IBM Research Labs around the world including Almaden Labs in San Jose, CA, Haifa Research Labs in Israel and Melbourne Research Lab in Australia. The goal of this project is to develop automated radiology and cardiology assistants of the future that help clinicians in their decision making.

Dr. Syeda-Mahmood graduated from the MIT AI Lab in 1993 with a Ph.D in Computer Science. Prior to IBM, she worked as a Research Staff Member at Xerox Webster Research Center, Webster, NY. She joined IBM Almaden Research Center in 1998.  Prior to coming to IBM, Dr. Syeda-Mahmood led the image indexing program at Xerox Research and was one of the early originators of the field of content-based image and video retrieval. Currently, she is working on applications of content-based retrieval in healthcare and medical imaging. Over the past 30 years, her research interests have been in a variety of areas relating to artificial intelligence including computer vision, image and video databases, medical image analysis, bioinformatics, signal processing, document analysis, and distributed computing frameworks. 


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


The explosion of algorithms to process medical images should draw increasing attention to validation methodologies, i.e. how to declare that a software tool actually works. In medical image analysis validation is complicated by several issues linked to the nature of the data, acquisition protocols, operators and devices, availability of annotated data, characteristics of the annotations (e.g. protocols, type of annotation) and others. Although frameworks for validation have been proposed (but not universally adopted), substantial questions remain open, including the overarching one:  how much can measurements (taken in a general sense) be trusted for subsequent decision making, be it statistical analysis, diagnosis etc? This talk attempts to capture the main issues behind validation, based on the 10+ years of interdisciplinary experience of the VAMPIRE group on retinal biomarkers, including crowdsourcing. The talk also aims to raise attention and interest on this crucial field of medical and healthcare data processing.

Slides – for more information, please contact Emanuele Trucco, VAMPIRE Project Director (Dundee), University of Dundee,


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.