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CEM500K, a large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning
Automated segmentation of cellular electron microscopy (EM) datasets remains a challenge. Supervised deep learning (DL) methods that rely on region-of-interest (ROI) annotations yield models that fail to generalize to unrelated datasets. Newer unsupervised DL algorithms require relevant pre-training...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
eLife Sciences Publications, Ltd
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032397/ https://www.ncbi.nlm.nih.gov/pubmed/33830015 http://dx.doi.org/10.7554/eLife.65894 |