Zusammenfassung
Semi-supervised learning offers great potential for medical image analysis, as it reduces the annotation burden for clinicians. In this work, we apply the state-of-the-art method FixMatch to chest X-ray and retinal image datasets. Our comparison with the supervised-only method is based on a fair hyperparameter tuning budget and includes label imbalance in the labeled set, thus simulating a practical evaluation setup. We find that unlabeled data can be used effectively for the retinal images, especially when using additional methods to counteract label imbalance in the unsupervised loss. In experiments with CheXpert, however, FixMatch does not provide substantial gains.
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Literatur
Sohn K, Berthelot D, Li CL, Zhang Z, Carlini N, Cubuk ED et al. Fixmatch: simplifying semi-supervised learning with consistency and confidence. ArXiv. 2020.
Liu Q, Yu L, Luo L, Dou Q, Heng PA. Semi-supervised medical image classification with relation-driven self-ensembling model. IEEE Trans Med Imaging. 2020;39(11):3429–40.
Gyawali PK, Ghimire S, Bajracharya P, Li Z, Wang L. Semi-supervised medical image classification with global latent mixing. Med Image Comput Comput Assist Interv. Springer. 2020:604–13.
Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C et al. Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison. Proc Conf AAAI Artif Intell. Vol. 33. (01). 2019:590–7.
Kaggle. Diabetic retinopathy detection [online]. 2015.
Voets M, Møllersen K, Bongo LA. Reproduction study using public data of: development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. PloS one. 2019;14(6):e0217541.
Johnson JM, Khoshgoftaar TM. Survey on deep learning with class imbalance. J Big Data. 2019;6(1):27.
Berthelot D, Carlini N, Cubuk ED, Kurakin A, Sohn K, Zhang H et al. Remixmatch: semisupervised learning with distribution alignment and augmentation anchoring. ArXiv. 2019.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2016.
Kingma DP, Ba J. Adam: a method for stochastic optimization. ArXiv. 2014.
Cubuk ED, Zoph B, Shlens J, Le QV. RandAugment: practical automated data augmentation with a reduced search space. ArXiv. 2019.
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© 2022 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Zenk, M., Zimmerer, D., Isensee, F., Jäger, P.F., Wasserthal, J., Maier-Hein, K. (2022). Realistic Evaluation of FixMatch on Imbalanced Medical Image Classification Tasks. In: Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_61
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DOI: https://doi.org/10.1007/978-3-658-36932-3_61
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