Abstract
Semi-supervised methods have an increasing impact on computer vision tasks to make use of scarce labels on large datasets, yet these approaches have not been well translated to medical imaging. Of particular interest, the MixMatch method achieves significant performance improvement over popular semi-supervised learning methods with scarce labels in the CIFAR-10 dataset. In a complementary approach, Nullspace Tuning on equivalence classes offers the potential to leverage multiple subject scans when the ground truth for the subject is unknown. This work is the first to (1) explore MixMatch with Nullspace Tuning in the context of medical imaging and (2) characterize the impacts of the methods with diminishing labels. We consider two distinct medical imaging domains: skin lesion diagnosis and lung cancer prediction. In both cases we evaluate models trained with diminishing labeled data using supervised, MixMatch, and Nullspace Tuning methods as well as MixMatch with Nullspace Tuning together. MixMatch with Nullspace Tuning together is able to achieve an AUC of 0.755 in lung cancer diagnosis with only 200 labeled subjects on the National Lung Screening Trial and a balanced multi-class accuracy of 77% with only 779 labeled examples on HAM10000. This performance is similar to that of the fully supervised methods when all labels are available. In advancing data driven methods in medical imaging, it is important to consider the use of current state-of-the-art semi-supervised learning methods from the greater machine learning community and their impact on the limitations of data acquisition and annotation.
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References
Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised learning (chapelle, o. et al., eds.; 2006) [book reviews]. IEEE Trans. Neural Netw. 20(3), 542–542 (2009)
Cubuk, E.D., et al.: AutoAugment: learning augmentation strategies from data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)
Berthelot, D., et al.: MixMatch: a holistic approach to semi-supervised learning. In: Advances in Neural Information Processing Systems (2019)
Zhang, H., et al.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)
Nath, V., et al.: Inter-scanner harmonization of high angular resolution DW-MRI using null space deep learning. In: Bonet-Carne, E., Grussu, F., Ning, L., Sepehrband, F., Tax, Chantal M.W. (eds.) MICCAI 2019. MV, pp. 193–201. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05831-9_16
Huo, Y., et al.: Coronary calcium detection using 3D attention identical dual deep network based on weakly supervised learning. In: Medical Imaging 2019: Image Processing. International Society for Optics and Photonics (2019)
Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018)
National Lung Screening Trial Research Team: The national lung screening trial: overview and study design. Radiology 258(1), 243–253 (2011)
Cireşan, D.C., et al.: Deep, big, simple neural nets for handwritten digit recognition. Neural Comput. 22(12), 3207–3220 (2010)
Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: ICDAR (2003)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)
Roth, H.R., et al.: Anatomy-specific classification of medical images using deep convolutional nets. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). IEEE (2015)
Frid-Adar, M., et al.: GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321, 321–331 (2018)
Frid-Adar, M., et al.: Synthetic data augmentation using GAN for improved liver lesion classification. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE (2018)
Bromley, J., et al.: Signature verification using a “siamese” time delay neural network. In: Advances in Neural Information Processing Systems (1994)
Wen, Y., Zhang, K., Li, Z., Qiao, Yu.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_31
Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005). IEEE (2005)
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006). IEEE (2006)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242 (2016)
Sajjadi, M., Javanmardi, M., Tasdizen, T.: Regularization with stochastic transformations and perturbations for deep semi-supervised learning. In: Advances in Neural Information Processing Systems (2016)
Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems (2017)
Lee, D.-H.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML (2013)
Miyato, T., et al.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1979–1993 (2018)
Chen, T., et al.: A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709 (2020)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Loshchilov, I., Hutter, F.: Fixing weight decay regularization in adam (2018)
Zhang, G., et al.: Three mechanisms of weight decay regularization. arXiv preprint arXiv:1810.12281 (2018)
Oliver, A., et al.: Realistic evaluation of deep semi-supervised learning algorithms. In: Advances in Neural Information Processing Systems (2018)
Paszke, A., et al.: Automatic differentiation in PyTorch (2017)
Iandola, F., et al.: DenseNet: implementing efficient ConvNet descriptor pyramids. arXiv preprint arXiv:1404.1869 (2014)
Li, K.M., Li, E.C.: Skin lesion analysis towards melanoma detection via end-to-end deep learning of convolutional neural networks. arXiv preprint arXiv:1807.08332 (2018)
Liao, F., et al.: Evaluate the malignancy of pulmonary nodules using the 3-D deep leaky noisy-or network. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3484–3495 (2019)
Gao, R., et al.: Distanced LSTM: time-distanced gates in long short-term memory models for lung cancer detection. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 310–318. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32692-0_36
Acknowledgements
This work was supported by the National Institutes of Health under award numbers R01EB017230, and T32EB001628, and in part by the National Center for Research Resources, Grant UL1 RR024975-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This study was in part using the resources of the Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University, Nashville, TN which is supported by NIH S10 RR031634.
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Hansen, C.B. et al. (2020). Semi-supervised Machine Learning with MixMatch and Equivalence Classes. In: Cardoso, J., et al. Interpretable and Annotation-Efficient Learning for Medical Image Computing. IMIMIC MIL3ID LABELS 2020 2020 2020. Lecture Notes in Computer Science(), vol 12446. Springer, Cham. https://doi.org/10.1007/978-3-030-61166-8_12
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DOI: https://doi.org/10.1007/978-3-030-61166-8_12
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