<ul><li>This work introduces IMPACT, a generic semantic similarity metric for multimodal medical image registration.</li><li>IMPACT compares deep learning-based features extracted from medical images without requiring task-specific training.</li><li>The proposed metric offers significant advantages such as robustness, scalability, and efficiency.</li><li>Evaluation on challenging registration tasks demonstrated improved anatomical alignment and increased robustness.</li></ul>

IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration

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