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A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer
- Source :
- Scientific Data, Vol 10, Iss 1, Pp 1-12 (2023)
- Publication Year :
- 2023
- Publisher :
- Nature Portfolio, 2023.
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Abstract
- Abstract The success of training computer-vision models heavily relies on the support of large-scale, real-world images with annotations. Yet such an annotation-ready dataset is difficult to curate in pathology due to the privacy protection and excessive annotation burden. To aid in computational pathology, synthetic data generation, curation, and annotation present a cost-effective means to quickly enable data diversity that is required to boost model performance at different stages. In this study, we introduce a large-scale synthetic pathological image dataset paired with the annotation for nuclei semantic segmentation, termed as Synthetic Nuclei and annOtation Wizard (SNOW). The proposed SNOW is developed via a standardized workflow by applying the off-the-shelf image generator and nuclei annotator. The dataset contains overall 20k image tiles and 1,448,522 annotated nuclei with the CC-BY license. We show that SNOW can be used in both supervised and semi-supervised training scenarios. Extensive results suggest that synthetic-data-trained models are competitive under a variety of model training settings, expanding the scope of better using synthetic images for enhancing downstream data-driven clinical tasks.
- Subjects :
- Science
Subjects
Details
- Language :
- English
- ISSN :
- 20524463
- Volume :
- 10
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Scientific Data
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.b05ef6157d2e4f2c957dde63e30cca82
- Document Type :
- article
- Full Text :
- https://doi.org/10.1038/s41597-023-02125-y