Robust Mitigation of Age-Dependent Confounding Effects via Sample-Difficulty Decorrelation

• URL: http://arxiv.org/abs/2605.19230v1
• Date: Tue, 19 May 2026 01:01:40 GMT
• Title: Robust Mitigation of Age-Dependent Confounding Effects via Sample-Difficulty Decorrelation
• Abstract summary: We propose a framework that mitigates the effects of age-dependent confounding by targeting spurious age linked trends.<n>We decorrelate age from dominant age difficulty trends using robust, Huber weighted affinity weights, attenuating confounding-driven shortcuts.<n>Our approach reduces age dependent true and false positive disparities with minimal AUC impact and remains robust to increasing train test age distribution shifts.
• Score: 0.8000485689206629
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Age dependent performance disparities in medical image classification often arise because age acts as a confounder, linking imaging morphology with disease prevalence. In practice, disparities can manifest as overdiagnosis at ages where disease prevalence is higher and underdiagnosis at ages where prevalence is lower, and can worsen under train test shifts in the age distribution. Conventional mitigation approaches that enforce strict age invariance may suppress diagnostically meaningful information encoded in age. We therefore propose a robust framework that mitigates the effects of age-dependent confounding by targeting spurious age linked trends rather than enforcing invariance. Following a warm-up phase, we characterize sample difficulty and model its age-dependent trends in a label-conditioned manner. We decorrelate age from dominant age difficulty trends using robust, Huber weighted affinity weights, attenuating confounding-driven shortcuts while preserving clinically meaningful, nonlinear age information. We further introduce an Age Coverage Score that scales the decorrelation penalty by minibatch age variance to ensure stable optimization under limited age diversity. Across two radiology datasets, our approach reduces age dependent true and false positive disparities with minimal AUC impact and remains robust to increasing train test age distribution shifts.

Related papers

• Dynamic causal discovery in Alzheimer's disease through latent pseudotime modelling [1.1222071637855076]
  We propose to apply an existing latent variable model to real-world Alzheimer's data, inferring a pseudotime that orders patients along a data-driven disease trajectory independent of chronological age.<n>Our framework reveals dynamic interactions between novel markers and established AD markers, enabling practical causal discovery despite violated assumptions.
  arXiv  Detail & Related papers  (2025-11-06T18:12:09Z)
• Bridging the gap in FER: addressing age bias in deep learning [0.562479170374811]
  We study age-related bias in deep FER models, with a particular focus on the elderly population.<n>Using Explainable AI (XAI) techniques, we identify systematic disparities in expression recognition and attention patterns.<n>Results show consistent improvements in recognition accuracy for elderly individuals.
  arXiv  Detail & Related papers  (2025-07-10T11:07:13Z)
• Ordinal Classification with Distance Regularization for Robust Brain Age Prediction [25.555190119033615]
  Age is one of the major known risk factors for Alzheimer's Disease (AD) Brain age, a measure derived from brain imaging reflecting structural changes due to aging, may have the potential to identify AD onset, assess disease risk, and plan targeted interventions. Deep learning-based regression techniques to predict brain age from magnetic resonance imaging (MRI) scans have shown great accuracy recently. These methods are subject to an inherent regression to the mean effect, which causes a systematic bias resulting in an overestimation of brain age in young subjects and underestimation in old subjects.
  arXiv  Detail & Related papers  (2023-10-25T20:39:07Z)
• Beyond Fairness: Age-Harmless Parkinson's Detection via Voice [32.18414430326228]
  Parkinson's disease (PD) manifests as speech and voice dysfunction. Deep learning models currently have fairness issues regarding different ages of onset. We present a new debiasing method using GradCAM-based feature masking combined with ensemble models.
  arXiv  Detail & Related papers  (2023-09-23T07:23:44Z)
• Pluralistic Aging Diffusion Autoencoder [63.50599304294062]
  Face aging is an ill-posed problem because multiple plausible aging patterns may correspond to a given input. This paper proposes a novel CLIP-driven Pluralistic Aging Diffusion Autoencoder to enhance the diversity of aging patterns.
  arXiv  Detail & Related papers  (2023-03-20T13:20:14Z)
• Adapting to Continuous Covariate Shift via Online Density Ratio Estimation [64.8027122329609]
  Dealing with distribution shifts is one of the central challenges for modern machine learning. We propose an online method that can appropriately reuse historical information. Our density ratio estimation method is proven to perform well by enjoying a dynamic regret bound.
  arXiv  Detail & Related papers  (2023-02-06T04:03:33Z)
• Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise [62.997667081978825]
  In high-risk environments, deep learning models need to be able to judge their uncertainty and reject inputs when there is a significant chance of misclassification. We conduct a rigorous evaluation of the most commonly used uncertainty and robustness methods for the classification of Whole Slide Images. We observe that ensembles of methods generally lead to better uncertainty estimates as well as an increased robustness towards domain shifts and label noise.
  arXiv  Detail & Related papers  (2023-01-03T11:34:36Z)
• Adaptive Mean-Residue Loss for Robust Facial Age Estimation [7.667560350473354]
  We propose a loss function for robust facial age estimation via distribution learning. Experimental results in the datasets FG-NET and CLAP2016 have validated the effectiveness of the proposed loss.
  arXiv  Detail & Related papers  (2022-03-31T16:28:34Z)
• LAE : Long-tailed Age Estimation [52.5745217752147]
  We first formulate a simple standard baseline and build a much strong one by collecting the tricks in pre-training, data augmentation, model architecture, and so on. Compared with the standard baseline, the proposed one significantly decreases the estimation errors. We propose a two-stage training method named Long-tailed Age Estimation (LAE), which decouples the learning procedure into representation learning and classification.
  arXiv  Detail & Related papers  (2021-10-25T09:05:44Z)
• STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization [76.57716281104938]
  We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
  arXiv  Detail & Related papers  (2020-12-08T21:21:47Z)
• Robustness to Spurious Correlations via Human Annotations [100.63051542531171]
  We present a framework for making models robust to spurious correlations by leveraging humans' common sense knowledge of causality. Specifically, we use human annotation to augment each training example with a potential unmeasured variable. We then introduce a new distributionally robust optimization objective over unmeasured variables (UV-DRO) to control the worst-case loss over possible test-time shifts.
  arXiv  Detail & Related papers  (2020-07-13T20:05:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.