Open Access
Issue
EPJ Web Conf.
Volume 341, 2025
2nd International Conference on Advent Trends in Computational Intelligence and Communication Technologies (ICATCICT 2025)
Article Number 01040
Number of page(s) 12
DOI https://doi.org/10.1051/epjconf/202534101040
Published online 20 November 2025
  1. D.-C. and L.W. He, "'Texture unit, texture spectrum, and texture analysis.' IEEE transactions on Geoscience and Remote Sensing 28.4 (1990): 509-512.," Ieee Trans. Geosci. Remote Sens., vol. 28, no. 4, pp. 509-512, 1990. [Google Scholar]
  2. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, vol. 1, pp. 886-893 vol. 1. [Google Scholar]
  3. CAMELYON17, "Grand Challenge. grand-challenge.org. https://camelyon17.grand-challenge.org/evaluation/challenge/leaderboard/. Accessed 3 Apr 2021." . [Google Scholar]
  4. B. Shi, L. J. Grimm, M. A. Mazurowski, J. A. Baker, J. R. Marks, L.M. King, C.C. Maley, E.S. Hwang, and J.Y. Lo, "Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features," J. Am. Coll. Radiol., vol. 15, no. 3, Part B, pp. 527-534, 2018. [Google Scholar]
  5. Z. Wang, B. Du, and Y. Guo, "Domain Adaptation With Neural Embedding Matching," IEEE Trans. Neural Networks Learn. Syst., vol. 31, no. 7, pp. 23872397, 2020. [Google Scholar]
  6. K. Weiss, T. M. Khoshgoftaar, and D. D. Wang, A survey of transfer learning, vol. 3, no. 1. Springer International Publishing, 2016. [Google Scholar]
  7. S. Panigrahi, A. Nanda, and T. Swarnkar, "A Survey on Transfer Learning," Smart Innov. Syst. Technol., vol. 194, pp. 781-789, 2021. [Google Scholar]
  8. F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong, and Q. He, "A Comprehensive Survey on Transfer Learning," Proc. IEEE, vol. 109, no. 1, pp. 43-76, 2021. [CrossRef] [Google Scholar]
  9. G. Wilson and D. J. Cook, "A Survey of Unsupervised Deep Domain Adaptation," ACM Trans. Intell. Syst. Technol., vol. 11, no. 5, 2020. [Google Scholar]
  10. T. Zhang, J. Cheng, H. Fu, Z. Gu, Y. Xiao, K. Zhou, S. Gao, R. Zheng, and J. Liu, "Noise Adaptation Generative Adversarial Network for Medical Image Analysis," IEEE Trans. Med. Imaging, vol. 39, no. 4, pp. 1149-1159, 2020. [Google Scholar]
  11. F. Jay, J.-P. Renou, O. Voinnet, and L. Navarro, "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Jun-Yan," Proc. IEEE Int. Conf. Comput. Vis., pp. 183-202, 2017. [Google Scholar]
  12. Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, "Domain-adversarial training of neural networks," Adv. Comput. Vis. Pattern Recognit., vol. 17, no. 9783319583464, pp. 189-209, 2017. [Google Scholar]
  13. Y. Zhang, Y. Wei, Q. Wu, P. Zhao, S. Niu, J. Huang, and M. Tan, "Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis," IEEE Trans. Image Process., vol. 29, pp. 7834-7844, 2020. [Google Scholar]
  14. Z. Wang, B. Du, W. Tu, L. Zhang, and D. Tao, "Incorporating Distribution Matching into Uncertainty for Multiple Kernel Active Learning," IEEE Trans. Knowl. Data Eng., vol. 33, no. 1, pp. 128-142, 2021. [Google Scholar]
  15. A. Chowdhury, J. Rosenthal, J. Waring, and R. Umeton, "Applying self-supervised learning to medicine: Review of the state of the art and medical implementations," Informatics, vol. 8, no. 3, pp. 1-29, 2021. [Google Scholar]
  16. G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sanchez, "A survey on deep learning in medical image analysis," Med. Image Anal., vol. 42, no. 1995, pp. 6088, 2017. [Google Scholar]
  17. J. Zhang, C. Li, M. M. Rahaman, Y. Yao, P. Ma, J. Zhang, X. Zhao, T. Jiang, and M. Grzegorzek, "A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches," Artif. Intell. Rev., vol. 55, no. 4, pp. 2875-2944, 2022. [Google Scholar]
  18. D. Agarwal, G. Marques, I. de la Torre-Diez, M. A. Franco Martin, B. Garcia Zapirain, and F. Martin Rodriguez, "Transfer learning for alzheimer's disease through neuroimaging biomarkers: A systematic review," Sensors, vol. 21, no. 21, pp. 1-31, 2021. [Google Scholar]
  19. J. M. Valverde, V. Imani, A. Abdollahzadeh, R. De Feo, M. Prakash, R. Ciszek, and J. Tohka, "Transfer learning in magnetic resonance brain imaging: A systematic review," J. Imaging, vol. 7, no. 4, pp. 1-21, 2021. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.