Abstract
Deep active learning (AL) is commonly used to reduce labeling costs in medical image analysis. Deep learning (DL) models typically exhibit a preference for learning from easy data and simple patterns before they learn from complex ones. However, existing AL methods often employ a fixed query strategy for sample selection, which may cause the model to focus too closely on challenging-to-classify data. The result is a deceleration of the convergence of DL models and an increase in the amount of labeled data required to train them. To address this issue, we propose a novel Adaptive Curriculum Query Strategy for AL in Medical Image Classification. During the training phase, our strategy leverages Curriculum Learning principles to initially prioritize the selection of a diverse range of samples to cover various difficulty levels, facilitating rapid model convergence. Once the distribution of the selected samples closely matches that of the entire dataset, the query strategy shifts its focus towards difficult-to-classify data based on uncertainty. This novel approach enables the model to achieve superior performance with fewer labeled samples. We perform extensive experiments demonstrating that our model not only requires fewer labeled samples but outperforms state-of-the-art models in terms of efficiency and effectiveness. The code is publicly available at https://github.com/HelenMa9998/Easy_hard_AL.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agarwal, S., Arora, H., Anand, S., Arora, C.: Contextual diversity for active learning. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVI 16. pp. 137–153. Springer (2020)
Aresta, G., Araújo, T., Kwok, S., Chennamsetty, S.S., Safwan, M., Alex, V., Marami, B., Prastawa, M., Chan, M., Donovan, M., et al.: Bach: Grand challenge on breast cancer histology images. Medical image analysis 56, 122–139 (2019)
Arpit, D., Jastrzębski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M.S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., et al.: A closer look at memorization in deep networks. In: International conference on machine learning. pp. 233–242. PMLR (2017)
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific data 4(1), 1–13 (2017)
Baldock, R., Maennel, H., Neyshabur, B.: Deep learning through the lens of example difficulty. Advances in Neural Information Processing Systems 34, 10876–10889 (2021)
Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: Fast and flexible image augmentations. Information 11(2) (2020). https://doi.org/10.3390/info11020125, https://www.mdpi.com/2078-2489/11/2/125
Citovsky, G., DeSalvo, G., Gentile, C., Karydas, L., Rajagopalan, A., Rostamizadeh, A., Kumar, S.: Batch active learning at scale. Advances in Neural Information Processing Systems 34, 11933–11944 (2021)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
Fuglede, B., Topsoe, F.: Jensen-shannon divergence and hilbert space embedding. In: International symposium onInformation theory, 2004. ISIT 2004. Proceedings. p. 31. IEEE (2004)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
Houlsby, N., Huszár, F., Ghahramani, Z., Lengyel, M.: Bayesian active learning for classification and preference learning. stat 1050, 24 (2011)
Jiang, L., Meng, D., Yu, S.I., Lan, Z., Shan, S., Hauptmann, A.: Self-paced learning with diversity. Advances in neural information processing systems 27 (2014)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Krueger, D., Ballas, N., Jastrzebski, S., Arpit, D., Kanwal, M.S., Maharaj, T., Bengio, E., Fischer, A., Courville, A.: Deep nets don’t learn via memorization (2017)
Kumar, M., Packer, B., Koller, D.: Self-paced learning for latent variable models. Advances in neural information processing systems 23 (2010)
Lewis, D.D.: A sequential algorithm for training text classifiers: Corrigendum and additional data. In: Acm Sigir Forum. vol. 29, pp. 13–19. ACM New York, NY, USA (1995)
Scheffer, T., Decomain, C., Wrobel, S.: Active hidden markov models for information extraction. In: International symposium on intelligent data analysis. pp. 309–318. Springer (2001)
Sener, O., Savarese, S.: Active learning for convolutional neural networks: A core-set approach. In: International Conference on Learning Representations (2018)
Settles, B.: Active learning literature survey (2009)
Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J.N., Wu, Z., Ding, X.: Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Medical Image Analysis 63, 101693 (2020)
Wang, X., Chen, Y., Zhu, W.: A survey on curriculum learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(9), 4555–4576 (2021)
Zhou, S.K., Greenspan, H., Shen, D.: Deep learning for medical image analysis. Academic Press (2023)
Acknowledgments
This research was conducted with the financial support of Science Foundation Ireland (SFI) to the Insight Centre for Data Analytics under Grant No. 12/RC/2289_P2.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ma, S., Du, H., Curran, K.M., Lawlor, A., Dong, R. (2024). Adaptive Curriculum Query Strategy for Active Learning in Medical Image Classification. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15011. Springer, Cham. https://doi.org/10.1007/978-3-031-72120-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-72120-5_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-72119-9
Online ISBN: 978-3-031-72120-5
eBook Packages: Computer ScienceComputer Science (R0)