GPC: Generative and General Pathology Image Classifier
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47401-9_20
Reference22 articles.
1. Cui, M., Zhang, D.Y.: Artificial intelligence and computational pathology. Lab. Invest. 101(4), 412–422 (2021). https://doi.org/10.1038/s41374-020-00514-0
2. Vuong, T.T.L., Song, B., Kim, K., Cho, Y.M., Kwak, J.T.: Multi-scale binary pattern encoding network for cancer classification in pathology images. IEEE J. Biomed. Health Inf. 26(3), 1152–1163 (2022). https://doi.org/10.1109/JBHI.2021.3099817
3. Vuong, T.T.L., Kim, K., Song, B., Kwak, J.T.: Joint categorical and ordinal learning for cancer grading in pathology images. Med. Image Anal. 73, 102206 (2021)
4. Vuong, T.T.L., Song, B., Kwak, J.T., Kim, K.: Prediction of epstein-barr virus status in gastric cancer biopsy specimens using a deep learning algorithm. JAMA Netw. Open 5, e2236408 (2022). https://doi.org/10.1001/jamanetworkopen.2022.36408
5. Vuong, T.T.L., Vu, Q.D., Jahanifar, M., Graham, S., Kwak, J.T., Rajpoot, N.: Impash: a novel domain-shift resistant representation for colorectal cancer tissue classification. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) Computer Vision - ECCV 2022 Workshops, pp. 543–555. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-25066-8_31
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