Purpose Cerebral microbleeds (CMBs) are critical imaging biomarkers for small vessel disease, but detection remains challenging due to small lesion size, variable MRI appearance, and annotation burden. This study developed a self-supervised learning (SSL) framework for robust CMB detection across multi-sequence MRI that generalizes to heterogeneous protocols while reducing dependence on labeled data.Materials and Methods An SSL framework (3D ResNet-18 with Barlow Twins loss) was pretrained on 2,450 unlabeled multi-sequence MRI scans (MICCAI 2022, VALDO, UK Biobank), then fine-tuned with only 400 labeled scans using a 3D U-Net for voxel-level detection. Performance was evaluated using ROC-AUC, sensitivity, false positives per scan, lesion-level F1-score, and cross-sequence generalization.Results The SSL framework achieved an AUC of 0.92 (95% CI: 0.90–0.94), sensitivity of 81%, and 1.1 false positives per scan—outperforming fully supervised (AUC 0.84) and semi-supervised (AUC 0.90) baselines. The model maintained robust performance across SWI (AUC 0.93), GRE (AUC 0.90), and 3T scanners (AUC 0.92), with lesion-level F1-scores of 78–84%. SSL pretraining enabled stable detection with as few as 100 labeled scans (AUC 0.90), demonstrating substantial annotation efficiency.Conclusion Self-supervised learning enables robust, generalizable CMB detection across heterogeneous multi-sequence MRI while significantly reducing annotation requirements. The framework's strong cross-sequence generalization supports its potential as a scalable clinical decision-support tool, though prospective validation in independent cohorts remains necessary.