ADVERSARIAL TRAINING FOR ROBUST DEFENSE IN CNN MODELS FOR LUNG AND COLON HISTOPATHOLOGICAL IMAGES
Abstract
Keywords
References
Kurishima, K., Miyazaki, K., Watanabe, H., Shiozawa, T., Ishikawa, H., Satoh, H., & Hizawa, N. (2018). Lung cancer patients with synchronous colon cancer. 8(1), 137–140. https://doi.org/10.3892/mco.2017.1471
Cancer Today (2020). Global Cancer Observatory (GLOBOCAN). Accessed May 03 2023, from http://gco.iarc.fr/today/home
Profil Kesehatan Indonesia Tahun (PUSDATIN) (2019). Kementerian Kesehatan Republik Indonesia. Accessed May 03 2023, from https://pusdatin.kemkes.go.id/resources/download/pusdatin/profil-kesehatan-indonesia/Profil-Kesehatan-indonesia-2019.pdfD
Hage Chehade, A., Abdallah, N., Marion, J.-M., Oueidat, M., & Chauvet, P. (2022). Lung and colon cancer classification using medical imaging: A feature engineering approach. Physical and Engineering Sciences in Medicine, 45(3), 729–746. https://doi.org/10.1007/s13246-022-01139-x
Hatuwal, B. K., & Thapa, H. C. (2020). Lung cancer detection using convolutional neural network on histopathological images. International Journal of Computer Trends & Technology, 68(10), 21–24. https://doi.org/10.14445/22312803/IJCTT-V68I10P104G
Ma, X., Niu, Y., Gu, L., Wang, Y., Zhao, Y., Bailey, J., & Lu, F. (2021). Understanding adversarial attacks on deep learning based medical image analysis systems. Pattern Recognition, 110, 107332. https://doi.org/10.1016/j.patcog.2020.107332I
Diyasa, I. G. S. M., Wahid, R. R., & Amiruddin, B. P. (2021). Grasping adversarial attacks on deep convolutional neural networks for cholangiocarcinoma classification. 2021 International Conference on e-Health and Bioengineering (EHB), 1–4. https://doi.org/10.1109/EHB52898.2021.9657589
Sipola, T., Puuska, S., & Kokkonen, T. (2020). Model fooling attacks against medical imaging: A short survey. Information & Security: An International Journal, 46(2), 215–224. https://doi.org/10.11610/isij.4615
Thangaraju, A., & Merkel, C. (2022). Exploring adversarial attacks and defenses in deep learning. 2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 1–6. https://doi.org/10.1109/CONECCT55679.2022.9865841
Wei, C., Sun, R., Li, P., & Wei, J. (2022). Analysis of the no-sign adversarial attack on the covid chest x-ray classification. 2022 International Conference on Image Processing and Media Computing (ICIPMC), 73–79. https://doi.org/10.1109/ICIPMC55686.2022.00022
Kaviani, S., Han, K. J., & Sohn, I. (2022). Adversarial attacks and defenses on AI in medical imaging informatics: A survey. Expert Systems with Applications, 198, 116815. https://doi.org/10.1016/j.eswa.2022. 116815
Lal, S., Rehman, S. U., Shah, J. H., Meraj, T., Rauf, H. T., Damaševičius, R., Mohammed, M. A., & Abdulkareem, K. H. (2021). Adversarial attack and defence through adversarial training and feature fusion for diabetic retinopathy recognition. Sensors, 21(11), 3922. https://doi.org/10.3390/s21113922
Shi, X., Peng, Y., Chen, Q., Keenan, T., Thavikulwat, A. T., Lee, S., Tang, Y., Chew, E. Y., Summers, R. M., & Lu, Z. (2022). Robust convolutional neural networks against adversarial attacks on medical images. Pattern Recognition, 132, 108923. https://doi.org/10.1016/j.patcog.2022.108923Q
Li, Y., & Liu, S. (2023). Adversarial attack and defense in breast cancer deep learning systems. Bioengineering, 10(8), 973. https://doi.org/10.3390/bioengineering10080973S
Borkowski, A. A., Bui, M. M., Thomas, L. B., Wilson, C. P., DeLand, L. A., & Mastorides, S. M. (2019). Lung and colon cancer histopathological image dataset (Lc25000). https://doi.org/10.48550/ARXIV.1912.12142
Masud, M., Sikder, N., Nahid, A.-A., Bairagi, A. K., & AlZain, M. A. (2021). A machine learning approach to diagnosing lung and colon cancer using a deep learning-based classification framework. Sensors, 21(3), 748. https://doi.org/10.3390/s21030748
He, W., Liu, T., Han, Y., Ming, W., Du, J., Liu, Y., Yang, Y., Wang, L., Jiang, Z., Wang, Y., Yuan, J., & Cao, C. (2022). A review: The detection of cancer cells in histopathology based on machine vision. Computers in Biology and Medicine, 146, 105636. https://doi.org/10.1016/j.compbiomed.2022.105636W
Vijh, S., Saraswat, M., & Kumar, S. (2021). A new complete color normalization method for H&E stained histopatholgical images. Applied Intelligence, 51(11), 7735–7748. https://doi.org/10.1007/s10489-021-02231-7Y
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014). Going deeper with convolutions. https://doi.org/10.48550/ARXIV.1409.4842
Ma, N., Zhang, X., Zheng, H.-T., & Sun, J. (2018). Shufflenet v2: Practical guidelines for efficient cnn architecture design. https://doi.org/10.48550/ARXIV.1807.11164
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep residual learning for image recognition. https://doi.org/10.48550/ARXIV.1512.03385
Chen, Y., Zhang, M., Li, J., & Kuang, X. (2022). Adversarial attacks and defenses in image classification: A practical perspective. 2022 7th International Conference on Image, Vision and Computing (ICIVC), 424–430. https://doi.org/10.1109/ICIVC55077.2022.9886997
Carlini, N., & Wagner, D. (2017). Towards evaluating the robustness of neural networks. 2017 IEEE Symposium on Security and Privacy (SP), 39–57. https://doi.org/10.1109/SP.2017.49
Moosavi-Dezfooli, S.-M., Fawzi, A., & Frossard, P. (2015). DeepFool: A simple and accurate method to fool deep neural networks. https://doi.org/10.48550/ARXIV.1511.04599
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2015). The limitations of deep learning in adversarial settings (arXiv:1511.07528; Versi 1). arXiv. https://doi.org/10.48550/arXiv.1511.07528
He, Z., Rakin, A. S., & Fan, D. (2019). Parametric noise injection: Trainable randomness to improve deep neural network robustness against adversarial attack. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 588–597. https://doi.org/10.1109/CVPR.2019.00068
Wang, J., Wang, C., Lin, Q., Luo, C., Wu, C., & Li, J. (2022). Adversarial attacks and defenses in deep learning for image recognition: A survey. Neurocomputing, 162–181. https://doi.org/10.1016/j.neucom.2022.09.004
Dalianis, H. (2018). Evaluation metrics and evaluation. Dalam H. Dalianis, Clinical Text Mining (hlm. 45–53). Springer International Publishing. https://doi.org/10.1007/978-3-319-78503-5_6
Hicks, S. A., Strümke, I., Thambawita, V., Hammou, M., Riegler, M. A., Halvorsen, P., & Parasa, S. (2022). On evaluation metrics for medical applications of artificial intelligence. Scientific Reports, 12(1), 5979. https://doi.org/10.1038/s41598-022-09954-8
Pacal, I., Karaboga, D., Basturk, A., Akay, B., & Nalbantoglu, U. (2020). A comprehensive review of deep learning in colon cancer. Computers in Biology and Medicine, 126, 104003. https://doi.org/10.1016/j.compbiomed.2020.104003
DOI: http://dx.doi.org/10.12962/j20882033.v35i2.19630
Refbacks
- There are currently no refbacks.
IPTEK Journal of Science and Technology by Lembaga Penelitian dan Pengabdian kepada Masyarakat, ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/jts.