Confidence estimation of classification based on the distribution of the neural network output layer

no code implementations • 14 Oct 2022 • Abdel Aziz Taha, Leonhard Hennig, Petr Knoth

In this paper, we propose novel methods that, given a neural network classification model, estimate uncertainty of particular predictions generated by this model.

Image Classification named-entity-recognition +3

On The Usage Of Average Hausdorff Distance For Segmentation Performance Assessment: Hidden Bias When Used For Ranking

no code implementations • 1 Sep 2020 • Orhun Utku Aydin, Abdel Aziz Taha, Adam Hilbert, Ahmed A. Khalil, Ivana Galinovic, Jochen B. Fiebach, Dietmar Frey, Vince Istvan Madai

We calculated the Kendall-rank-correlation-coefficient between the segmentation ranking and the number of errors in each simulated segmentation.

Image Segmentation Medical Image Segmentation +2

Why rankings of biomedical image analysis competitions should be interpreted with care

no code implementations • 6 Jun 2018 • Lena Maier-Hein, Matthias Eisenmann, Annika Reinke, Sinan Onogur, Marko Stankovic, Patrick Scholz, Tal Arbel, Hrvoje Bogunovic, Andrew P. Bradley, Aaron Carass, Carolin Feldmann, Alejandro F. Frangi, Peter M. Full, Bram van Ginneken, Allan Hanbury, Katrin Honauer, Michal Kozubek, Bennett A. Landman, Keno März, Oskar Maier, Klaus Maier-Hein, Bjoern H. Menze, Henning Müller, Peter F. Neher, Wiro Niessen, Nasir Rajpoot, Gregory C. Sharp, Korsuk Sirinukunwattana, Stefanie Speidel, Christian Stock, Danail Stoyanov, Abdel Aziz Taha, Fons van der Sommen, Ching-Wei Wang, Marc-André Weber, Guoyan Zheng, Pierre Jannin, Annette Kopp-Schneider

International challenges have become the standard for validation of biomedical image analysis methods.