Weakly Supervised Learning Algorithms for Digital Pathology <\/p>\n
Assoc. Prof. Dr. Selim Aksoy
\nComputer Engineering<\/p>\n
May 10, 1:40 p.m.
\nPlace: EA-409 <\/p>\n
Learning classifiers from large image sets has been a popular problem in computer vision and machine learning. The commonly employed supervised learning framework typically uses manually selected image regions with no ambiguity regarding their class labels. However, collecting sufficiently large number of examples for classes with high within-class variance and low between-class variance is not always possible. We will present weakly supervised learning algorithms for the classification of whole slide breast pathology images having both localization and labeling uncertainties. <\/p>\n
Dr. Selim Aksoy received the B.S. degree from Middle East Technical University in 1996, and the M.S. and Ph.D. degrees from the University of Washington, Seattle, USA, in 1998 and 2001, respectively. Since 2004, he has been with the Department of Computer Engineering, Bilkent University, where he is currently an Associate Professor. He spent 2013 as a Visiting Associate Professor at the Department of Computer Science & Engineering, University of Washington. His research interests include computer vision, pattern recognition, and machine learning with applications to medical imaging and remote sensing. <\/p>\n","protected":false},"excerpt":{"rendered":"
Weakly Supervised Learning Algorithms for Digital Pathology Assoc. Prof. Dr. Selim Aksoy Computer Engineering May 10, 1:40 p.m. Place: EA-409 Learning classifiers from large image sets has been a popular problem in computer vision and machine learning. The commonly employed supervised learning framework typically uses manually selected image regions with [\u2026] <\/p>\n