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Teeth2Point achieves a 1.44 DSC point improvement in segmentation accuracy for challenging dental cases, showcasing a breakthrough in handling missing or misaligned teeth.
SpineSegDiff not only matches the best in vertebral segmentation but also reveals critical insights into degenerative disc conditions through uncertainty mapping.
Sparse annotations can yield results nearly indistinguishable from dense ones, with SA-VIS achieving over 1% improvement in AP on state-of-the-art benchmarks.
Trustworthy super-resolution in surgery is now achievable, with a model-agnostic method that identifies and mitigates unreliable reconstructions in real-time.
Slap pseudo-labels on your unlabeled medical images and cut your annotation budget in half when adapting vision-language models.
