You are all cordially invited to this week’s AMLab seminar, on Friday January 26 at 15:30 in C3.165 (so the day, time and location of the idea club). There Veronika Cheplygina (Eindhoven) will give a talk titled “Challenges of multiple instance learning in medical image analysis”. Afterwards there are the usual drinks and snacks!
Abstract: Data is often only weakly annotated: for example, for a medical image, we might know the patient’s diagnosis, but not where the abnormalities are located. Multiple instance learning (MIL), is aimed at learning classifiers from such data. In this talk, I will share a number of lessons I have learnt about MIL so far: (1) researchers do not agree on what MIL is, (2) there is no “one size fits all” approach (3) we need more thorough evaluation methods. I will give examples from several applications, including computer-aided diagnosis in chest CT images. I will also briefly discuss my work on crowdsourcing medical image annotations, and why MIL might be useful in this case.
Veronika Cheplygina is an assistant professor at the Medical Image Analysis group, Eindhoven University of Technology since February 2017. She received her Ph.D. from the Delft University of Technology for her thesis “Dissimilarity-Based Multiple Instance Learning” in 2015. As part of her PhD, she was a visiting researcher at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. From 2015 to 2016 she was a postdoc at the Biomedical Imaging Group Rotterdam, Erasmus MC. Her research interests are centered around learning scenarios where few labels are available, such as multiple instance learning, transfer learning, and crowdsourcing. Next to research, Veronika blogs about academic life at http://www.veronikach.com