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Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis
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Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis

Changjian Shui
Justin Szeto
Raghav Mehta
Douglas L. Arnold
Tal Arbel
ArXiv (abs)PDFHTML

Papers citing "Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis"

5 / 5 papers shown
Title
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles
Xing Shen
Hengguan Huang
Brennan Nichyporuk
Tal Arbel
MedIm
136
4
0
01 Jul 2025
Achieve Fairness without Demographics for Dermatological Disease Diagnosis
Achieve Fairness without Demographics for Dermatological Disease Diagnosis
Ching-Hao Chiu
Yu-Jen Chen
Yawen Wu
Yiyu Shi
Tsung-Yi Ho
79
6
0
16 Jan 2024
Understanding Fairness Surrogate Functions in Algorithmic Fairness
Understanding Fairness Surrogate Functions in Algorithmic Fairness
Wei Yao
Zhanke Zhou
Zhicong Li
Bo Han
Yong Liu
88
5
0
17 Oct 2023
Debiasing Counterfactuals In the Presence of Spurious Correlations
Debiasing Counterfactuals In the Presence of Spurious Correlations
Amar Kumar
Nima Fathi
Raghav Mehta
Brennan Nichyporuk
Jean-Pierre Falet
Sotirios A. Tsaftaris
Tal Arbel
CMLMedIm
55
5
0
21 Aug 2023
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
Benjamin Lambert
Florence Forbes
A. Tucholka
Senan Doyle
Harmonie Dehaene
M. Dojat
110
90
0
05 Oct 2022