# Domain-Adaptive MRI Learning Model for Precision Diagnosis of CNS Tumors

## TL;DR

This paper introduces a new AI model that improves the accuracy and reliability of diagnosing brain tumors using MRI scans across different hospitals and scanners.

## Contribution

The novel contribution is a domain-adaptive MRI learning model combining CNN and transformer architectures with adversarial alignment and feature harmonization for robust tumor diagnosis.

## Key findings

- DA-MLM achieved 94.8% accuracy on BraTS 2020 dataset, outperforming previous benchmarks by 2–4%.
- Segmentation Dice scores improved by 2–7% over existing CNN and transformer methods on REMBRANDT dataset.
- DA-MLM showed 40–60% less performance degradation under noise, contrast shifts, and motion artifacts compared to other models.

## Abstract

Background: Diagnosing CNS tumors through MRI is limited by significant variability in scanner hardware, acquisition protocols, and intensity characteristics at clinical centers, resulting in substantial domain shifts that lead to diminished reliability for automated models. Methods: We present a Domain-Adaptive MRI Learning Model (DA-MLM) consisting of an adversarially aligned hybrid 3D CNN–transformer encoder with contrastive regularization and covariance-based feature harmonization. Varying sequence MRI inputs (T1, T1ce, T2, and FLAIR) were inputted to multi-scale convolutional layers followed by global self-attention to effectively capture localized tumor structure and long-range spatial context, with domain adaptation that harmonizes feature distribution across datasets. Results: On the BraTS 2020 dataset, we found DA-MLM achieved 94.8% accuracy, 93.6% macro-F1, and 96.2% AUC, improving upon previously established benchmarks by 2–4%. DA-MLM also attained Dice score segmentation of 93.1% (WT), 91.4% (TC), and 89.5% (ET), improving upon 2–3.5% for CNN and transformer methods. On the REMBRANDT dataset, DA-MLM achieved 92.3% accuracy with segmentation improvements of 3–7% over existing U-Net and expert annotations. Robustness testing indicated 40–60% less degradation under noise, contrast shift, and motion artifacts, and synthetic shifts in scanner location showed negligible performance impairment (<0.06). Cross-domain evaluation also demonstrated 5–11% less degradation than existing methods. Conclusions: In summary, DA-MLM demonstrates improved accuracy, segmentation fidelity, and robustness to perturbations, as well as strong cross-domain generalization indicating the suitability for deployment in multicenter MRI applications where variation in imaging performance is unavoidable.