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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
eLife Sciences Publications Ltd
2021-04-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/65894 |