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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
Abstract
Breast cancer is one of the deadliest types of cancer among women worldwide. Early detection plays a crucial role in increasing the chances of successful treatment and reducing the risk of death. Various efforts have been made by both the general public and medical professionals to raise awareness, promote early screening, and ensure timely medical intervention. With advances in technology, the use of computer-based systems, particularly in the field of medical image analysis, has become increasingly important. One such application is histopathological image analysis to support the diagnostic process in breast cancer cases. Histopathological image classification has gained significant attention from researchers in recent years, and various machine learning and deep learning techniques have been applied to improve its accuracy. Convolutional Neural Networks (CNNs), as part of the deep learning framework, have shown promising results in identifying tissue patterns in histopathological images. However, despite their high accuracy, CNNs are often less interpretable, making it difficult to understand the reasoning behind their predictions—especially when dealing with subtle features such as small spots, dots, or fine lines that may be overlooked. This study addresses these limitations by proposing a method that not only classifies histopathological images with high accuracy but also enhances readability through localization techniques. The goal is to make the classification process more transparent and clinically useful. Using widely recognized datasets like BreakHIS, the proposed method achieves 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. https://doi.org/10.1158/1541-7786.MCR-23-0411
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- 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. https://doi.org/10.1109/TNNLS.2018.2869164
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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. https://doi.org/10.1158/1541-7786.MCR-23-0411
T. Agustin, “Potensi Metabolit Aktif Dalam Sayuran Cruciferous Untuk Menghambat Pertumbuhan Sel Kanker,” 2020.
E. Marfianti, “Peningkatan Pengetahuan Kanker Payudara dan Ketrampilan Periksa Payudara Sendiri (SADARI) untuk Deteksi Dini Kanker Payudara di Semutan Jatimulyo Dlingo,” 2021. https://doi.org/10.20885/jamali.vol3.iss1.art4
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. https://doi.org/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.
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. https://doi.org/10.7150/jca.95589
A. Indah Sari, “Skrining Mamografi dan Mortalitas Kanker Payudara,” vol. 7, no. 7, p. 11, 2022.
R. Resmiati and T. Arifin, “SISTEMASI: Jurnal Sistem Informasi Klasifikasi Pasien Kanker Payudara Menggunakan Metode Support Vector Machine dengan Backward Elimination,” 2021. https://doi.org/10.32520/stmsi.v10i2.1238
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. https://doi.org/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. https://doi.org/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. https://doi.org/10.1007/s42979-022-01536-9
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. https://doi.org/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. https://doi.org/10.3390/healthcare10112300
S. A. Alanazi et al., “Boosting Breast Cancer Detection Using Convolutional Neural Network,” J Healthc Eng, vol. 2021, 2021. https://doi.org/10.1155/2021/5528622
P. Kumar, S. Srivastava, R. K. Mishra, and Y. P. Sai, “End-to-end improved convolutional neural network model for breast cancer detection using mammographic data,” Journal of Defense Modeling and Simulation, vol. 19, no. 3, pp. 375–384, Jul. 2022. https://doi.org/10.1177/1548512920973268
D. Kumar Saha, T. Hossain, M. Safran, S. Alfarhood, M. F. Mridha, and D. Che, “Segmentation for mammography classification utilizing deep convolutional neural network,” BMC Med Imaging, vol. 24, no. 1, Dec. 2024. https://doi.org/10.1186/s12880-024-01510-2
J. Nouri Pour, M. A. Pourmina, and M. N. Moghaddasi, “Improving Breast Cancer Detection with Convolutional Neural Networks and Modified ResNet Architecture,” Curr Med Imaging Rev, vol. 20, Apr. 2024. https://doi.org/10.2174/0115734056290499240402102301
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. https://doi.org/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. https://doi.org/10.23919/ICACT60172.2024.10471995
Z. Shao et al., “TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification,” Jun. 2021. https://doi.org/10.48550/arXiv.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. https://doi.org/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. https://doi.org/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. https://doi.org/10.48550/arXiv.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. 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). https://doi.org/10.3390/electronics12204323