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Explicit modular spatial verification can dramatically enhance diagnostic accuracy in medical imaging, outperforming traditional end-to-end models by a staggering margin.
Identifying the right model for clinical deployment without target labels is fraught with challenges, leaving a significant performance gap even with advanced selection methods.
Selecting the best unsupervised domain adaptation algorithm for medical imaging can now be achieved without any labeled data, enhancing clinical deployment efficiency.