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Label-Free Coreset Selection with Foundation Models for Efficient Annotation in Computational Pathology
Computational pathology has the potential to improve clinical outcomes through a demonstrated increase in diagnostic and prognostic accuracy. However, the development and validation of deep learning algorithms still require annotated data, a costly procedure involving expert pathologists who already
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