GAME-Net: an ensemble deep learning framework integrating Generative Autoencoders and attention mechanisms for automated brain tumor segmentation in MRI

The findings indicate that enriched representation learning and attention-driven feature refinement substantially elevate segmentation accuracy on heterogeneous MRI data. The integration of unsupervised learning within the pipeline supported improved generalization across variable imaging conditions. The demonstrated performance suggests strong potential for clinical utility, although broader validation across external datasets is recommended to further substantiate generalizability.