# Detect and Correct Bias in Multi-Site Neuroimaging Datasets

arXiv: https://arxiv.org/abs/2002.05049 · HTML: https://mlsanity.com/paper/2002.05049/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2020.

## Built on by (most cited first, 30 of 45)

- [A Survey on Bias in Visual Datasets](https://mlsanity.com/paper/2107.07919/) (2021)
- [Probable Domain Generalization via Quantile Risk Minimization](https://mlsanity.com/paper/2207.09944/) (2022)
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- [Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review](https://mlsanity.com/paper/2209.13177/) (2022)
- [Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation](https://mlsanity.com/paper/2103.12857/) (2021)
- [Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions](https://mlsanity.com/paper/2201.06505/) (2022)
- [A First Look at Dataset Bias in License Plate Recognition](https://mlsanity.com/paper/2208.10657/) (2022)
- [Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary](https://mlsanity.com/paper/2101.07235/) (2021)
- [Metadata Normalization](https://mlsanity.com/paper/2104.09052/) (2021)
- [Harmonization with Flow-based Causal Inference](https://mlsanity.com/paper/2106.06845/) (2021)
- [Contrastive learning for regression in multi-site brain age prediction](https://mlsanity.com/paper/2211.08326/) (2022)
- [Statistical quantification of confounding bias in predictive modelling](https://mlsanity.com/paper/2111.00814/) (2021)
- [Fairness-Aware Data Augmentation for Cardiac MRI using Text-Conditioned Diffusion Models](https://mlsanity.com/paper/2403.19508/) (2024)
- [Benchmarking CNN on 3D Anatomical Brain MRI: Architectures, Data Augmentation and Deep Ensemble Learning](https://mlsanity.com/paper/2106.01132/) (2021)
- [Efficacy of MRI data harmonization in the age of machine learning. A multicenter study across 36 datasets](https://mlsanity.com/paper/2211.04125/) (2022)
- [RegBN: Batch Normalization of Multimodal Data with Regularization](https://mlsanity.com/paper/2310.00641/) (2023)
- [Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework](https://mlsanity.com/paper/2402.06875/) (2024)
- [Federated brain tumor segmentation: an extensive benchmark](https://mlsanity.com/paper/2410.17265/) (2024)
- [Invariant Shape Representation Learning For Image Classification](https://mlsanity.com/paper/2411.12201/) (2024)
- [Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants](https://mlsanity.com/paper/2205.04871/) (2022)
- [Debiased Noise Editing on Foundation Models for Fair Medical Image Classification](https://mlsanity.com/paper/2403.06104/) (2024)
- [Domain-invariant feature learning in brain MR imaging for content-based image retrieval](https://mlsanity.com/paper/2501.01326/) (2025)
- [Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI](https://mlsanity.com/paper/2108.06518/) (2021)
- [Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg](https://mlsanity.com/paper/2504.10244/) (2025)
- [Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization](https://mlsanity.com/paper/2506.07378/) (2025)
- [Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysis](https://mlsanity.com/paper/2512.01534/) (2025)
- [Individualized Mapping of Aberrant Cortical Thickness via Stochastic Cortical Self-Reconstruction](https://mlsanity.com/paper/2403.06837/) (2024)
- [A Natural Language Processing Approach to Support Biomedical Data Harmonization: Leveraging Large Language Models](https://mlsanity.com/paper/2411.02730/) (2024)
- [Active Sampling for MRI-based Sequential Decision Making](https://mlsanity.com/paper/2505.04586/) (2025)
- [Cortex-Grounded Diffusion Models for Brain Image Generation](https://mlsanity.com/paper/2601.19498/) (2026)

## Abstract (arXiv)

The desire to train complex machine learning algorithms and to increase the statistical power in association studies drives neuroimaging research to use ever-larger datasets. The most obvious way to increase sample size is by pooling scans from independent studies. However, simple pooling is often ill-advised as selection, measurement, and confounding biases may creep in and yield spurious correlations. In this work, we combine 35,320 magnetic resonance images of the brain from 17 studies to examine bias in neuroimaging. In the first experiment, Name That Dataset, we provide empirical evidence for the presence of bias by showing that scans can be correctly assigned to their respective dataset with 71.5% accuracy. Given such evidence, we take a closer look at confounding bias, which is often viewed as the main shortcoming in observational studies. In practice, we neither know all potential confounders nor do we have data on them. Hence, we model confounders as unknown, latent variables. Kolmogorov complexity is then used to decide whether the confounded or the causal model provides the simplest factorization of the graphical model. Finally, we present methods for dataset harmonization and study their ability to remove bias in imaging features. In particular, we propose an extension of the recently introduced ComBat algorithm to control for global variation across image features, inspired by adjusting for population stratification in genetics. Our results demonstrate that harmonization can reduce dataset-specific information in image features. Further, confounding bias can be reduced and even turned into a causal relationship. However, harmonziation also requires caution as it can easily remove relevant subject-specific information. Code is available at https://github.com/ai-med/Dataset-Bias.