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Classification of Breast Cancer Histopathology Images with Attention-Based Multiple Instance Learning Method
Corresponding Author(s) : Safira Hasna Setiyani
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 10, No. 4, November 2025 (Article in Progress)
Abstract
Breast cancer is one of the cancers with the highest mortality rate among women worldwide. Early detection plays a crucial role in improving the chances of successful treatment and reducing the risk of death. Numerous efforts have been made both by the general public and healthcare professionals to promote awareness, early screening, and timely medical intervention. In line with technological advancements, the use of computer-based systems, particularly in the field of medical image analysis, has become increasingly important. One such application is the analysis of histopathology images to support the diagnosis process in breast cancer cases. Histopathological image classification has attracted considerable attention from researchers in recent years, and a variety of machine learning and deep learning techniques have been applied to improve its accuracy. Convolutional Neural Networks (CNNs), as part of deep learning frameworks, have shown promising results in identifying tissue patterns in histopathology images. However, despite their high accuracy, CNNs often lack interpretability, making it difficult to understand the reasoning behind their decisions—especially when dealing with subtle features such as small spots, dots, or fine lines, which may go undetected. This study addresses those limitations by proposing a method that not only classifies histopathology images with high accuracy but also improves interpretability through localization techniques. The goal is to make the classification process more transparent and clinically useful. Using widely recognized datasets such as BreakHIS, the proposed method achieved a classification accuracy of up to 97.50%, demonstrating its potential as a reliable tool in medical diagnostics and breast cancer research.
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- J. S. Brown, S. R. Amend, R. H. Austin, R. A. Gatenby, E. U. Hammarlund, and K. J. Pienta, “Updating the Definition of Cancer,” Molecular Cancer Research, vol. 21, no. 11, pp. 1142–1147, 2023, doi: 10.1158/1541-7786.MCR-23-0411.
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References
J. S. Brown, S. R. Amend, R. H. Austin, R. A. Gatenby, E. U. Hammarlund, and K. J. Pienta, “Updating the Definition of Cancer,” Molecular Cancer Research, vol. 21, no. 11, pp. 1142–1147, 2023, doi: 10.1158/1541-7786.MCR-23-0411.
T. Agustin, “Potensi Metabolit Aktif Dalam Sayuran Cruciferous Untuk Menghambat Pertumbuhan Sel Kanker,” 2020. [Online]. Available: http://jurnal.globalhealthsciencegroup.com/index.php/JPPP
E. Marfianti, “Peningkatan Pengetahuan Kanker Payudara dan Ketrampilan Periksa Payudara Sendiri (SADARI) untuk Deteksi Dini Kanker Payudara di Semutan Jatimulyo Dlingo,” 2021. [Online]. Available: https://journal.uii.ac.id/JAMALI
J. Zhou, Z. Wu, D. Aili, L. Wang, and T. Liu, “Exploration of the carcinogenetic and immune role of CHK1 in human cancer,” J Cancer, vol. 15, no. 18, pp. 5927–5941, 2024, doi: 10.7150/jca.93930.
W. Ramdhani, D. Bona, R. B. Musyaffa, and C. Rozikin, “Klasifikasi Penyakit Kangker Payudara Menggunakan Algoritma K-Nearest Neighbor,” Jurnal Ilmiah Wahana Pendidikan, vol. 2022, no. 12, pp. 445–452, 2022, doi: 10.5281/zenodo.6968420.
Z. Zhang et al., “A new clinical prognosis model for breast cancer with ADSS as the hub gene,” J Cancer, vol. 15, no. 18, pp. 5910–5926, 2024, doi: 10.7150/jca.95589.
A. Indah Sari, “Skrining Mamografi dan Mortalitas Kanker Payudara,” vol. 7, no. 7, p. 11, 2022, doi: 10.36418/syntax-literate.v7i11.11932.
R. Resmiati and T. Arifin, “SISTEMASI: Jurnal Sistem Informasi Klasifikasi Pasien Kanker Payudara Menggunakan Metode Support Vector Machine dengan Backward Elimination,” 2021. [Online]. Available: http://sistemasi.ftik.unisi.ac.id
H. Sung et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA Cancer J Clin, vol. 71, no. 3, pp. 209–249, May 2021, doi: 10.3322/caac.21660.
B. Zhang, H. Shi, and H. Wang, “Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach,” 2023, Dove Medical Press Ltd. doi: 10.2147/JMDH.S410301.
N. Aidossov et al., “An Integrated Intelligent System for Breast Cancer Detection at Early Stages Using IR Images and Machine Learning Methods with Explainability,” SN Comput Sci, vol. 4, no. 2, Mar. 2023, doi: 10.1007/s42979-022-01536-9.
M. Javaid, A. Haleem, R. Pratap Singh, R. Suman, and S. Rab, “Significance of machine learning in healthcare: Features, pillars and applications,” International Journal of Intelligent Networks, vol. 3, pp. 58–73, Jan. 2022, doi: 10.1016/j.ijin.2022.05.002.
I. Idawati, D. P. Rini, A. Primanita, and T. Saputra, “Klasifikasi Kanker Payudara Menggunakan Metode Convolutional Neural Network (CNN) dengan Arsitektur VGG-16,” Jurnal Sistem Komputer dan Informatika (JSON), vol. 5, no. 3, p. 529, Apr. 2024, doi: 10.30865/json.v5i3.7553.
X. Xu, Q. Guo, Z. Li, and D. Li, “Uncertainty Ordinal Multi-Instance Learning for Breast Cancer Diagnosis,” Healthcare (Switzerland), vol. 10, no. 11, Nov. 2022, doi: 10.3390/healthcare10112300.
N. Yudistira, M. S. Kavitha, J. Rajan, and T. Kurita, “Attention-effective multiple instance learning on weakly stem cell colony segmentation,” Intelligent Systems with Applications, vol. 17, Feb. 2023, doi: 10.1016/j.iswa.2023.200187.
T. P. Theodore Armand, S. Bhattacharjee, and H. C. Kim, “Overview of the Potentials of Multiple Instance Learning in Cancer Diagnosis: Applications, Challenges, and Future Directions,” in International Conference on Advanced Communication Technology, ICACT, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 419–425. doi: 10.23919/ICACT60172.2024.10471995.
Z. Shao et al., “TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification,” Jun. 2021, [Online]. Available: http://arxiv.org/abs/2106.00908
Y. Kim, T. Wang, D. Xiong, X. Wang, and S. Park, “Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences,” BMC Bioinformatics, vol. 23, no. 1, Dec. 2022, doi: 10.1186/s12859-022-05012-2.
M. A. Carbonneau, E. Granger, and G. Gagnon, “Bag-Level Aggregation for Multiple-Instance Active Learning in Instance Classification Problems,” IEEE Trans Neural Netw Learn Syst, vol. 30, no. 5, pp. 1441–1451, May 2019, doi: 10.1109/TNNLS.2018.2869164.
L. Cai, S. Huang, Y. Zhang, J. Lu, and Y. Zhang, “Rethinking Attention-Based Multiple Instance Learning for Whole-Slide Pathological Image Classification: An Instance Attribute Viewpoint,” Mar. 2024, [Online]. Available: http://arxiv.org/abs/2404.00351
H. Xiang, J. Shen, Q. Yan, M. Xu, X. Shi, and X. Zhu, “Multi-scale representation attention based deep multiple instance learning for gigapixel whole slide image analysis,” Med Image Anal, vol. 89, p. 102890, 2023, doi: https://doi.org/10.1016/j.media.2023.102890.
S. Fatima, S. Ali, and H. C. Kim, “A Comprehensive Review on Multiple Instance Learning,” Oct. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/electronics12204323.
A. Wijoyo, A. Y. Saputra, S. Ristanti, R. Sya’ban, M. Amalia, and R. Febriansyah, “Pembelajaran Machine Learning,” Jurnal Ilmu Komputer dan Science, vol. Volume 3, 2024.
Xiang, H., Shen, J., Yan, Q., Xu, M., Shi, X., & Zhu, X. (2023). Multi-scale representation attention based deep multiple instance learning for gigapixel whole slide image analysis. Medical Image Analysis, 89, 102890. https://doi.org/https://doi.org/10.1016/j.media.2023.102890
Xiong, D., Zhang, Z., Wang, T., & Wang, X. (2021). A comparative study of multiple instance learning methods for cancer detection using T-cell receptor sequences. In Computational and Structural Biotechnology Journal (Vol. 19, pp. 3255– 3268). Elsevier B.V. https://doi.org/10.1016/j.csbj.2021.05.038