The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks
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Cites
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
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He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137
Hosmer, D. W., & Lemeshow, S. (2000) · doi:10.1002/0471722146 · Cited by 20,302
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Aggregated Residual Transformations for Deep Neural Networks
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.634 · Cited by 12,465
Arber, D. A., Orazi, A., Hasserjian, R., Thiele, J., Borowitz, M. J., Le Beau, M. M., Bloomfield, C. D., Cazzola, M., & Vardiman, J. W. (2016) · Blood · doi:10.1182/blood-2016-03-643544 · Cited by 10,119
Döhner, H., Estey, E., Grimwade, D., Amadori, S., Appelbaum, F. R., Büchner, T., Dombret, H., Ebert, B. L., Fenaux, P., Larson, R. A., et al. (2017) · Blood · doi:10.1182/blood-2016-08-733196 · Cited by 5,775
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968
Cited by
Artificial intelligence for digital and computational pathology
Song, A. H., Jaume, G., Williamson, D. F. K., Lu, M. Y., Vaidya, A., Miller, T. R., & Mahmood, F. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00096-8 · Cited by 356
A deep learning-based system for assessment of serum quality using sample images
Yang, C., Li, D., Sun, D., Zhang, S., Zhang, P., Xiong, Y., Zhao, M., Qi, T., Situ, B., & Zheng, L. (2022) · Clinica Chimica Acta · doi:10.1016/j.cca.2022.04.010 · Cited by 19
Metadata
- Authors
- Matek, C., Schwarz, S., Spiekermann, K., & Marr, C.
- Venue
- Nature Machine Intelligence
- Published
- 2019-11-12
- Volume
- 1
- Issue
- 11
- Pages
- 538-544
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W2986235590
- Cited by
- 281
- References
- 34
- FWCI
- 10.50
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks. https://app.cochat.ai/papers/10.1038/s42256-019-0101-9
- The paper
- Matek, C., Schwarz, S., Spiekermann, K., & Marr, C. (2019). Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks. Nature Machine Intelligence. https://doi.org/10.1038/s42256-019-0101-9
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
