Abstract
Deep learning-based methods have been widely used for medical image classification. However, in clinical practice, rare diseases are usually underrepresented with limited labeled data, which result in long-tailed medical datasets and significantly degrade the performance of deep classification networks. Previous strategies employ re-sampling or re-weighting techniques to alleviate this issue by increasing the influence of underrepresented classes and reducing the influence of overrepresented ones. Still, poor performance may occur due to overfitting of the tail classes. Further, Mixup is employed to introduce additional information into model training. Despite considerable improvements, the significant noise in medical images means that random batch mixing may introduce ambiguity into training, thereby impair the performance. This observation motivates us to develop a fine-grained mixing approach. In this paper we present Curriculum of Class-wise Mixup (CCMix), a novel method for addressing the challenge of long-tailed distributions. CCMix leverages a novel curriculum that takes into account both the degree of mixing and the class-wise performance to identify the ideal Mixup proportions of different classes. Our method’s simplicity enables its effortless integration with existing long-tailed recognition techniques. Comprehensive experiments on two long-tailed medical image classification datasets demonstrate that our method, requiring no modifications to the framework structure or algorithmic details, achieves state-of-the-art results across diverse long-tailed classification benchmarks. The source code is available at https://github.com/sirileeee/CCMix.
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
References
Ahn, S., Ko, J., Yun, S.Y.: CUDA: curriculum of data augmentation for long-tailed recognition. arXiv preprint arXiv:2302.05499 (2023)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Choi, H.K., Choi, J., Kim, H.J.: TokenMixup: efficient attention-guided token-level data augmentation for transformers. Adv. Neural. Inf. Process. Syst. 35, 14224–14235 (2022)
Chu, P., Bian, X., Liu, S., Ling, H.: Feature space augmentation for long-tailed data. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 694–710. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58526-6_41
Codella, N., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1902.03368 (2019)
Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: RandAugment: practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 702–703 (2020)
Cui, J., Liu, S., Tian, Z., Zhong, Z., Jia, J.: ResLT: residual learning for long-tailed recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45(3), 3695–3706 (2022)
Cui, J., Zhong, Z., Liu, S., Yu, B., Jia, J.: Parametric contrastive learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 715–724 (2021)
Cui, J., Zhong, Z., Tian, Z., Liu, S., Yu, B., Jia, J.: Generalized parametric contrastive learning. IEEE Trans. Pattern Anal. Mach. Intell. (2023)
Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9268–9277 (2019)
Estabrooks, A., Jo, T., Japkowicz, N.: A multiple resampling method for learning from imbalanced data sets. Comput. Intell. 20(1), 18–36 (2004)
Galdran, A., Carneiro, G., González Ballester, M.A.: Balanced-MixUp for highly imbalanced medical image classification. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 323–333. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87240-3_31
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)
Huang, C., Li, Y., Loy, C.C., Tang, X.: Learning deep representation for imbalanced classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5375–5384 (2016)
Huang, Y., Lin, L., Cheng, P., Lyu, J., Tam, R., Tang, X.: Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation. Diagnostics 13(10) (2023). https://doi.org/10.3390/diagnostics13101664. https://www.mdpi.com/2075-4418/13/10/1664
Kang, B., Li, Y., Xie, S., Yuan, Z., Feng, J.: Exploring balanced feature spaces for representation learning. In: International Conference on Learning Representations (2020)
Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. arXiv preprint arXiv:1910.09217 (2019)
Karthick, M., Sohier, D.: APTOS 2019 blindness detection (2019). Kaggle https://kaggle.com/competitions/aptos2019-blindness-detection. Go to reference in chapter
Kim, J.H., Choo, W., Jeong, H., Song, H.O.: Co-Mixup: saliency guided joint mixup with supermodular diversity. arXiv preprint arXiv:2102.03065 (2021)
Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D.D., Chen, M.: Medical image classification with convolutional neural network. In: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 844–848. IEEE (2014)
Lin, L., et al.: BSDA-net: a boundary shape and distance aware joint learning framework for segmenting and classifying OCTA images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 65–75. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_7
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., Yu, S.X.: Large-scale long-tailed recognition in an open world. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2537–2546 (2019)
Ma, T., et al.: A simple long-tailed recognition baseline via vision-language model. arXiv preprint arXiv:2111.14745 (2021)
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 270–279. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01424-7_27
Uddin, A., Monira, M., Shin, W., Chung, T., Bae, S.H., et al.: SaliencyMix: a saliency guided data augmentation strategy for better regularization. arXiv preprint arXiv:2006.01791 (2020)
Verma, V., et al.: Manifold mixup: better representations by interpolating hidden states. In: International Conference on Machine Learning, pp. 6438–6447. PMLR (2019)
Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: CutMix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6023–6032 (2019)
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)
Zhang, Z., Pfister, T.: Learning fast sample re-weighting without reward data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 725–734 (2021)
Zhu, J., Wang, Z., Chen, J., Chen, Y.P.P., Jiang, Y.G.: Balanced contrastive learning for long-tailed visual recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6908–6917 (2022)
Acknowledgement
This study was supported by the Shenzhen Basic Research Program (JCYJ20190809120205578); the National Natural Science Foundation of China (62071210); the Shenzhen Science and Technology Program (RCYX20210609103056042); the Shenzhen Basic Research Program (JCYJ20200925153847004); the Shenzhen Science and Technology Innovation Committee (KCXFZ2020122117340001).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, S. et al. (2024). CCMix: Curriculum of Class-Wise Mixup for Long-Tailed Medical Image Classification. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14349. Springer, Cham. https://doi.org/10.1007/978-3-031-45676-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-45676-3_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45675-6
Online ISBN: 978-3-031-45676-3
eBook Packages: Computer ScienceComputer Science (R0)
