The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Deep learning microscopy
Open accessAbstract
We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field-of-view and depth-of-field. After its training, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design. We blindly tested this deep learning approach using various tissue samples that are imaged with low-resolution and wide-field systems, where the network rapidly outputs an image with remarkably better resolution, matching the performance of higher numerical aperture lenses, also significantly surpassing their limited field-of-view and depth-of-field. These results are transformative for various fields that use microscopy tools, including e.g., life sciences, where optical microscopy is considered as one of the most widely used and deployed techniques. Beyond such applications, our presented approach is broadly applicable to other imaging modalities, also spanning different parts of the electromagnetic spectrum, and can be used to design computational imagers that get better and better as they continue to image specimen and establish new transformations among different modes of imaging.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
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Mastering the game of Go with deep neural networks and tree search
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Dermatologist-level classification of skin cancer with deep neural networks
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Identity Mappings in Deep Residual Networks
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All-optical machine learning using diffractive deep neural networks
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Self-citationDeep learning for cellular image analysis
Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0403-1 · Cited by 1,225
Deep learning for tomographic image reconstruction
Wang, G., Ye, J. C., & De Man, B. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00273-z · Cited by 531
Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media
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Metadata
- Authors
- Rivenson, Y., Göröcs, Z., Günaydin, H., Zhang, Y., Wang, H., & Ozcan, A.
- Venue
- Optica
- Publisher
- Optica Publishing Group
- Published
- 2017-11-20
- Volume
- 4
- Issue
- 11
- Pages
- 1437
- ISSN
- 2334-2536 (ISSN-L)
- OpenAlex
- W2614949728
- Cited by
- 643
- References
- 12
- FWCI
- 31.11
- Type
- journal article
- Language
- en
- Open access
- gold
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- Rivenson, Y., Göröcs, Z., Günaydin, H., Zhang, Y., Wang, H., & Ozcan, A. (2017). Deep learning microscopy. Optica. https://doi.org/10.1364/optica.4.001437
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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