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Medical University of Vienna
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QUAM-SM reveals hidden vulnerabilities in medical image segmentation models, enhancing uncertainty quantification and clinical reliability.
Flow-matching adaptation transforms noisy OCT images into high-quality outputs, revolutionizing automated analysis in ophthalmology.
Finally, a meta-learning approach that uses readily available negative control samples can close the persistent domain gap in biomedical imaging, making deep learning models practically usable across different experimental batches.
