The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study
Open accessAbstract
: In summary, after training with a large dataset, the DCNN VGG-16 model showed great potential in facilitating PTC diagnosis from cytological images. The contours, perimeter, area and mean of pixel intensity of PTC in fragmented images were more than the benign nodules.
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Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Haugen, B. R., Alexander, E. K., Bible, K. C., Doherty, G. M., Mandel, S. J., Nikiforov, Y. E., Pacini, F., Randolph, G. W., Sawka, A. M., Schlumberger, M., et al. (2016) · Thyroid® · doi:10.1089/thy.2015.0020 · Cited by 16,535
Siegel, R., Naishadham, D., & Jemal, A. (2012) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.20138 · Cited by 10,679
Deep learning for classification of colorectal polyps on whole-slide images
Korbar, B., Olofson, A. M., Miraflor, A. P., Nicka, C. M., Suriawinata, M. A., Torresani, L., Suriawinata, A. A., & Hassanpour, S. (2017) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_34_17 · Cited by 349
Cited by
Emerging role of deep learning‐based artificial intelligence in tumor pathology
Jiang, Y., Yang, M., Wang, S., Li, X., & Sun, Y. (2020) · Cancer Communications · doi:10.1002/cac2.12012 · Cited by 438
Ali, M. S., Miah, M. S., Haque, J., Rahman, M. M., & Islam, M. K. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100036 · Cited by 415
Understanding of Convolutional Neural Network (CNN): A Review
Purwono, P., Ma'arif, A., Rahmaniar, W., Fathurrahman, H. I. K., Frisky, A. Z. K., & Haq, Q. M. U. (2023) · International Journal of Robotics and Control Systems · doi:10.31763/ijrcs.v2i4.888 · Cited by 319
Machine learning and deep learning algorithms for skin cancer classification from dermoscopic images
Bechelli, S., & Delhommelle, J. (2022) · Bioengineering · doi:10.3390/bioengineering9030097 · Cited by 181
Claret, S. P. A., Dharmian, J. P., & Manokar, A. M. (2024) · Egyptian Journal of Medical Human Genetics · doi:10.1186/s43042-024-00522-5 · Cited by 40
Metadata
- Authors
- Guan, Q., Wang, Y., Ping, B., Li, D., Du, J., Qin, Y., Lu, H., Wan, X., & Xiang, J.
- Venue
- Journal of Cancer
- Publisher
- Ivyspring International Publisher
- Published
- 2019
- Volume
- 10
- Issue
- 20
- Pages
- 4876-4882
- ISSN
- 1837-9664 (ISSN-L)
- OpenAlex
- W2972621596
- Cited by
- 288
- References
- 4
- FWCI
- 12.43
- Type
- journal article
- Language
- en
- Open access
- gold
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- Guan, Q., Wang, Y., Ping, B., Li, D., Du, J., Qin, Y., Lu, H., Wan, X., & Xiang, J. (2019). Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. Journal of Cancer. https://doi.org/10.7150/jca.28769
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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.
