NuCLS (Nucleus Classification, Localization and Segmentation)

Introduced by Amgad et al. in NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation

The NuCLS dataset contains over 220,000 labeled nuclei from breast cancer images from TCGA. These nuclei were annotated through the collaborative effort of pathologists, pathology residents, and medical students using the Digital Slide Archive. These data can be used in several ways to develop and validate algorithms for nuclear detection, classification, and segmentation, or as a resource to develop and evaluate methods for interrater analysis.

Data from both single-rater and multi-rater studies are provided. For single-rater data we provide both pathologist-reviewed and uncorrected annotations. For multi-rater datasets we provide annotations generated with and without suggestions from weak segmentation and classification algorithms.

Source: Amgad et al.

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Source: Amgad et al..

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