Abstract
We show how a simple autoencoder based deep network with a contrastive loss can effectively learn representations in a continual/incremental manner with limited labelling. This is of particular interest to the biomedical imaging research community, for whom the visual task is often a binary decision (healthy vs. disease) with limited quantity data and costly labelling. For such applications, the proposed method provides a light-weight option of 1) representing patterns with relatively few training samples using a novel collaborative contrastive loss function 2) update the autoencoder based deep network in an unsupervised fashion for continual learning for new incoming data. We overcome the drawbacks of existing methods through planned technical design, and demonstrate the efficacy of the proposed method on three histology image classification tasks (lung, colon, breast cancer) with SOTA results.
The authors are funded by the UKRI Innovate UK DART Lung Health Programme.
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References
Liu, X., et al.: A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Dig. Health 1(6), 271–297 (2019)
Gonzalez, C., Sakas, G., Mukhopadhyay, A.: What is Wrong with Continual Learning in Medical Image Segmentation?. arXiv:2010.11008 (2020)
Baweja, C., Glocker, B., Kamnitsas, K.: Towards continual learning in medical imaging. arXiv:1811.02496 (2018)
Bhalgat, Y., Shah, M., Awate, S.: Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks. arXiv:1812.11302 (2018)
Ye, M., Zhang, X., Yuen, P.C., Chang, S.-F.: Unsupervised embedding learning via invariant and spreading instance feature. In: Proc, CVPR (2019)
Zhuang, C., Zhai, A.L., Yamins, D.: Local aggregation for unsupervised learning of visual embeddings. In Proc, ICCV (2019)
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In Proc, CVPR (2006)
Oord, A., Li, Y., Vinyals, O.: Representation Learning with Contrastive Predictive Coding. arXiv:1807.03748v2 (2019)
Gutmann, M., Hyvarinen, A.: Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Proc, AISTATS (2010)
Hjelm, R.D., Fedorov, A., Marchildon, S.L., Grewal, K., Trischler, A., Bengio, Y.: Learning deep representations by mutual information estimation and maximization. In Proc, ICLR (2019)
Bachman, P., Hjelm, R.D., Buchwalter, W.: Learning representations by maximizing mutual information across views. arXiv:1906.00910 (2019)
Wu, Z., Xiong, Y., Yu, S., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In Proc, CVPR (2018)
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum Contrast for Unsupervised Visual Representation Learning. In Proc, CVPR (2020)
Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: Which helps face recognition? In Proc, ICCV (2011)
Chakraborti, T., McCane, B., Mills, S., Pal, U.: A Generalised Formulation for Collaborative Representation of Image Patches (GP-CRC). In Proc, BMVC (2017)
Aresta, G., et al.: BACH: Grand challenge on breast cancer histology images. Medical Image Analysis 56, 122–139 (2019)
Borkowski, A.A., Bui, M.M., Thomas, L.B., Wilson, C.P., DeLand, L.A., Mastorides, S.M.: Lung and Colon Cancer Histopathological Image Dataset (LC25000). arXiv:1912.12142 (2019)
Henaff, O.J., Razavi, A., Doersch, C., Eslami, S.M., Oord, A.: Data-efficient image recognition with contrastive predictive coding. arXiv:1905.09272 (2019)
Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. arXiv:1906.05849 (2019)
Rey, D., Neuhauser, M.: Wilcoxon-Signed-Rank Test. Springer, International Encyclopedia of Statistical Science (2011). https://doi.org/10.1007/978-3-642-04898-2
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Chakraborti, T., Gleeson, F., Rittscher, J. (2021). Contrastive Representations for Continual Learning of Fine-Grained Histology Images. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_1
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