# Mitigating MRI Domain Shift in Sex Classification: A Deep Learning Approach with ComBat Harmonization

## TL;DR

This study addresses the challenge of domain shift in MRI-based sex classification by applying ComBat harmonization, significantly improving cross-dataset model performance and emphasizing the importance of domain adaptation in neuroimaging AI.

## Contribution

The paper demonstrates that ComBat harmonization effectively reduces domain shift in MRI data, enhancing the generalizability of deep learning models for sex classification across different datasets.

## Key findings

- ComBat harmonization improves cross-domain accuracy from 0.50 to 0.61.
- Deep learning models perform well within datasets but poorly across datasets without harmonization.
- Feature visualization confirms reduced domain discrepancy after harmonization.

## Abstract

Deep learning models for medical image analysis often suffer from performance degradation when applied to data from different scanners or protocols, a phenomenon known as domain shift. This study investigates this challenge in the context of sex classification from 3D T1-weighted brain magnetic resonance imaging (MRI) scans using the IXI and OASIS3 datasets. While models achieved high within-domain accuracy (around 0.95) when trained and tested on a single dataset (IXI or OASIS3), we demonstrate a significant performance drop to chance level (about 0.50) when models trained on one dataset are tested on the other, highlighting the presence of a strong domain shift. To address this, we employed the ComBat harmonization technique to align the feature distributions of the two datasets. We evaluated three state-of-the-art 3D deep learning architectures (3D ResNet18, 3D DenseNet, and 3D EfficientNet) across multiple training strategies. Our results show that ComBat harmonization effectively reduces the domain shift, leading to a substantial improvement in cross-domain classification performance. For instance, the cross-domain balanced accuracy of our best model (ResNet18 3D with Attention) improved from approximately 0.50 (chance level) to 0.61 after harmonization. t-SNE visualization of extracted features provides clear qualitative evidence of the reduced domain discrepancy post-harmonization. This work underscores the critical importance of domain adaptation techniques for building robust and generalizable neuroimaging AI models.

---
Source: https://tomesphere.com/paper/2508.20300