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صفحه اصلی / سی و دومین کنفرانس ملی و دهمین کنفرانس بین المللی مهندسی زیست پزشکی ایران

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

نویسندگان :

Peyman Sharifian¹ Mohammad Saber Azimi² Masoud Noroozi³ Alireza Karimian⁴ Hossein Arabi⁵

1- دانشگاه اصفهان 2- دانشگاه شهید بهشتی 3- دانشگاه اصفهان 4- دانشگاه اصفهان 5- دانشگاه ژنو

کلمات کلیدی :

Deep Learning،Sex Classification،Combat Harmonization،Domain Adaptation،Magnetic Resonance Imaging

چکیده :

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. Cross-domain balanced accuracy improved from ~0.50 to 0.61 after ComBat, a modest yet meaningful gain that moves the model from chance-level failure toward more reliable generalization while remaining below clinical utility. This work underscores the critical importance of domain adaptation techniques for building robust and generalizable neuroimaging AI models.