Imbalance multiclass problem: a robust feature enhancement-based framework for liver lesion classification-Reference-Cited by-同舟云学术

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Imbalance multiclass problem: a robust feature enhancement-based framework for liver lesion classification

Published:2024-03-30 Issue:2 Volume:30 Page:
ISSN:0942-4962
Container-title:Multimedia Systems
language:en
Short-container-title:Multimedia Systems

Author:

Hu Rui,Song Yuqing,Liu Yi,Zhu Yan,Feng Nuo,Qiu Chengjian,Han Kai,Teng Qiaoying,Haq Imran Ul,Liu Zhe

Funder

National Natural Science Foundation of China

Key RESEARCH and development program for social development

Six Talent Peaks Project in Jiangsu Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00530-024-01291-2.pdf

Reference41 articles.

1. Ryerson, A.B., Eheman, C.R., Altekruse, S.F., Ward, J.W., Jemal, A., Sherman, R.L., Henley, S.J., Holtzman, D., Lake, A., Noone, A.-M.: Annual report to the nation on the status of cancer, 1975–2012, featuring the increasing incidence of liver cancer. Cancer 122(9), 1312–1337 (2016)

2. Romero, F.P., Diler, A., Bisson-Gregoire, G., Turcotte, S., Lapointe, R., Vandenbroucke-Menu, F., Tang, A., Kadoury, S.: End-to-end discriminative deep network for liver lesion classification. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1243–1246. IEEE (2019)

3. Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: Gan-based synthetic medical image augmentation for increased cnn performance in liver lesion classification. Neurocomputing 321, 321–331 (2018)

4. Balasubramanian, P.K., Lai, W.-C., Seng, G.H., Selvaraj, J.: Apestnet with mask r-cnn for liver tumor segmentation and classification. Cancers 15(2), 330 (2023)

5. Ma, C., Liu, Z., Song, Y., Qiu, C., Yu, A., Zhang, J.: Self-supervision adversarial learning network for liver lesion classification. In: Artificial Intelligence and Security: 7th International Conference, ICAIS 2021, Dublin, Ireland, July 19–23, 2021, Proceedings, Part I 7, pp. 540–552. Springer (2021)

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