Improving the fairness and robustness of deep learning classification models through bias mitigation and post hoc calibration - Summary - MDSpire
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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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Objective:

To examine two approaches to mitigating age-related confounding in MRI-based Alzheimer’s disease classification and evaluate four post hoc probability-calibration methods.

Approach:
  • Data: The models used whole-brain, voxel-wise gray matter probability maps from preprocessed T1-weighted MRI scans in the ADNI2/GO, AIBL, and DELCODE cohorts. All scans underwent the same automated preprocessing pipeline.
  • Bias mitigation: Three classification models used a common CNN feature-extractor architecture to compare debiasing strategies. The study developed and implemented two methodologies to mitigate age-related confounding; the supplied context does not describe both methods in detail.
  • Calibration: The study evaluated four post hoc probability-calibration methods: two parametric and two nonparametric.
Key Findings:
  • The supplied context describes the study’s aims and methods but reports no comparative results for the bias-mitigation or calibration methods.
Interpretation:

The study’s rationale identifies age-related confounding in MRI-based classification and notes that softmax outputs may not correspond to observed outcome frequencies, motivating evaluation of post hoc calibration.

Limitations:
  • The provided article context ends during the CF-Net methods description and contains no results or discussion.
  • Age was the only confounding variable explicitly addressed in the modeling framework.

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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