Abstract
Classify image into benign and malignant is one of the basic image processing tools in digital pathology for breast cancer diagnosis. Deep learning methods have received more attention recently by training with large-scale labeled datas, but collecting and annotating clinical data is professional and time-consuming. The proposed work develops a deep active learning framework to reduce the annotation burden, where the method actively selects the valuable unlabeled samples to be annotated instead of random selecting. Besides, compared with standard query strategy in previous active learning methods, the proposed query strategy takes advantage of manual labeling and auto-labeling to emphasize the confidence boosting effect. We validate the proposed work on a public histopathological image dataset. The experimental results demonstrate that the proposed method is able to reduce up to 52% labeled data compared with random selection. It also outperforms deep active learning method with standard query strategy in the same tasks.
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References
Lakhani, S.R., Ellis. I.O., Schnitt, S.: WHO classification of tumours of the breast. In: International Agency for Research on Cancer, WHO Press, Lyon (2012)
Veta, M., Pluim, J.P.W., van Diest, P.J.: Breast cancer histopathology image analysis: a review. IEEE Trans. Biomed. Eng. 2(5), 1400–1411 (2014)
Chen, H., Dou, Q., Wang, X.: Mitosis detection in breast cancer histology images via deep cascaded networks. In: Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona, pp. 1160–1166. AAAI Press (2016)
Wang, D., Khoslam, A., Gargeya, R.: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718 (2016)
Spanhol, F.A., Oliveira, L.S.: Breast cancer histopathological image classification using convolutional neural networks. In: International Joint Conference on Neural Networks, Vancouver, BC, Canada, pp. 2561–2567. IEEE (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, pp. 1097–1105. Curran Associates Inc. (2012)
Bayramoglu, N., Kannala, J., Heikkila, J.: Deep learning for magnification independent breast cancer histopathology image classification. In: International Conference on Pattern Recognition (ICPR), Cancun, Mexico, pp. 2440–2445. IEEE (2017)
Spanhol, F.A., Cavalin P.R., Oliveira, L.S.: Deep features for breast cancer histopathological image classification. In: IEEE International Conference on Systems, Los Angeles, CA, USA, pp. 1868–1873 (2017)
Weil, B., Han, Z., He, X.: Deep learning model based breast cancer histopathological image classification. In: 2nd IEEE International Conference on Cloud Computing and Big Data Analysis, Chengdu, China, pp. 348–353. IEEE (2017)
Huang, Y., Zheng, H., Liu, C.: Epithelium-stroma classification via convolutional neural networks and unsupervised domain adaptation in histopathological images. IEEE J. Biomed. Health Inform. 21(6), 1625–1632 (2017)
Yue Huang, Han Zheng, Chi Liu: Epithelium-stroma classification in histopathological images via convolutional neural networks and self-taught learning. In: IEEE International Conference on Acoustics, pp. 1073–1077. IEEE, New Orleans, LA, USA (2017)
Huang, S.-J., Jin, R., Zhou, Z.-H.: Active learning by querying informative and representative examples. In: International Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, pp. 892–900. Curran Associates Inc. (2010)
Spanhol, F., Oliveira, L., Petitjean, C.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 61(7), 1455–1462 (2016)
Krizheevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, pp. 1097–1105. Curran Associates Inc. (2012)
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Du, B., Qi, Q., Zheng, H., Huang, Y., Ding, X. (2018). Breast Cancer Histopathological Image Classification via Deep Active Learning and Confidence Boosting. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_11
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