Abstract
Deep Learning has achieved a state-of-the-art performance in medical imaging analysis but requires a large number of labelled images to obtain good adequate performance. However, such labelled images are costly to acquire in time, labour, and human expertise. We propose a novel practical Bayesian Active Learning approach using Dropweights and overall bias-corrected uncertainty measure to suggest which unlabelled image to annotate. Experiments were done on Brain Tumour MR images, Microscopic Cell Image classification, Fluoro-chromogenic cytokeratin-Ki-67 double staining cancer images and Retina fundus image segmentation tasks. We demonstrate that our active learning technique is equally successful or better than other existing active learning approaches in high dimensional data to reduce the image labelling effort significantly. We believe Bayesian deep active learning framework with very few annotated samples in a practical way will benefit clinicians to obtain fast and accurate image annotation with confidence.
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References
Aggarwal, C., Kong, X., Gu, Q., Han, J., Yu, P.: Active learning: a survey. In: Aggarwal, C.C. (ed.) Data Classification: Algorithms and Applications, pp. 571–606. CRC Press (2014)
Cheng, J., et al.: Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PLoS ONE 11(6), e0157112 (2016)
Cheng, J., et al.: Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE 10(10), e0140381 (2015)
Di Scandalea, M.L., Perone, C.S., Boudreau, M., Cohen-Adad, J.: Deep active learning for axon-myelin segmentation on histology data. arXiv preprint arXiv:1907.05143 (2019)
Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: 33rd International Conference on Machine Learning, ICML 2016, vol. 3, pp. 1651–1660 (2016)
Gal, Y., Islam, R., Ghahramani, Z.: Deep Bayesian active learning with image data. arXiv preprint arXiv:1703.02910 (2017)
Ghoshal, B., Lindskog, C., Tucker, A.: Estimating uncertainty in deep learning for reporting confidence: an application on cell type prediction in testes based on proteomics. In: Berthold, M.R., Feelders, A., Krempl, G. (eds.) IDA 2020. LNCS, vol. 12080, pp. 223–234. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44584-3_18
Ghoshal, B., Tucker, A., Sanghera, B., Lup Wong, W.: Estimating uncertainty in deep learning for reporting confidence to clinicians in medical image segmentation and diseases detection. Comput. Intell. (2020)
Houlsby, N., Huszár, F., Ghahramani, Z., Lengyel, M.: Bayesian active learning for classification and preference learning. Stat 1050, 24 (2011)
Ma, J., et al.: How distance transform maps boost segmentation CNNs: an empirical study. In: Arbel, T., Ayed, I.B., de Bruijne, M., Descoteaux, M., Lombaert, H., Pal, C. (eds.) Medical Imaging with Deep Learning. Proceedings of Machine Learning Research, vol. 121, pp. 479–492. PMLR (2020). http://proceedings.mlr.press/v121/ma20b.html
Rajaraman, S., et al.: Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images. PeerJ 6, e4568 https://doi.org/10.7717/peerj.4568 (2018)
Ren, P., Xiao, Y., Chang, X., Huang, P.Y., Li, Z., Chen, X., Wang, X.: A survey of deep active learning. arXiv preprint arXiv:2009.00236 (2020)
Settles, B.: Active learning literature survey. Computer Sciences Technical report 1648, University of Wisconsin, Department of Computer Science (2009)
Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)
Valkonen, M., et al.: Cytokeratin-supervised deep learning for automatic recognition of epithelial cells in breast cancers stained for ER, PR, and Ki-67. IEEE Trans. Med. Imaging 39(2), 534–542 (2019)
Wang, H., Yeung, D.Y.: Towards Bayesian deep learning: a survey. arXiv preprint arXiv:1604.01662 (2016)
Wang, K., Zhang, D., Li, Y., Zhang, R., Lin, L.: Cost-effective active learning for deep image classification. IEEE Trans. Circ. Syst. Video Technol. 27(12), 2591–2600 (2016)
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Ghoshal, B., Swift, S., Tucker, A. (2021). Bayesian Deep Active Learning for Medical Image Analysis. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_4
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