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Enhancing Fairness and Robustness in Deep Learning Classifiers with Bias Mitigation and Post Hoc Calibration

  • By

  • Majid Ramedani

  • Jaya C. Terli

  • Ole H. Martensen

  • Tobias Strauß

  • Katharina Buerger

  • Peter Dechent

  • Emrah Düzel

  • Klaus Fliessbach

  • Maria Gemenetzi

  • Wenzel Glanz

  • Julian Hellmann-Regen

  • Stefan Hetzer

  • Enise I. Incesoy

  • Daniel Janowitz

  • Marie T. Kronmüller

  • Christoph Laske

  • Falk Lüsebrink

  • Robert Perneczky

  • Oliver Peters

  • Lukas Preis

  • Josef Priller

  • Boris-Stephan Rauchmann

  • Ayda Rostamzadeh

  • Anja Schneider

  • Björn H. Schott

  • Sebastian Sodenkamp

  • Annika Spottke

  • Eike J. Spruth

  • Sophia Stöcklein

  • Jens Wiltfang

  • Frank Jessen

  • Stefan J. Teipel

  • Martin Dyrba

  • October 17, 2026

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Original Source(s)

Improving the fairness and robustness of deep learning classification models through bias mitigation and post hoc calibration

  • by Majid Ramedani, Jaya C. Terli, Ole H. Martensen, Tobias Strauß, Katharina Buerger, Peter Dechent, Emrah Düzel, Klaus Fliessbach, Maria Gemenetzi, Wenzel Glanz, Julian Hellmann-Regen, Stefan Hetzer, Enise I. Incesoy, Daniel Janowitz, Marie T. Kronmüller, Christoph Laske, Falk Lüsebrink, Robert Perneczky, Oliver Peters, Lukas Preis, Josef Priller, Boris-Stephan Rauchmann, Ayda Rostamzadeh, Anja Schneider, Björn H. Schott, Sebastian Sodenkamp, Annika Spottke, Eike J. Spruth, Sophia Stöcklein, Jens Wiltfang, Frank Jessen, Stefan J. Teipel, Martin Dyrba

  • October 17, 2026

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