Abstract
Predicting the presence of a disease in volumetric images is an essential task in medical imaging. The use of state-of-the-art techniques like deep convolutional neural networks (CNN) for such tasks is challenging due to limited supervised training data and high memory usage. This paper presents a weakly supervised solution that can be used in learning deep CNN features for volumetric image classification. In the proposed method, we use extreme value theory to model the feature distribution of the images without a pathology and use it to identify positive instances in an image that contains pathology. The experimental results show that the proposed method can learn classifiers that have similar performance to a fully supervised method and have significantly better performance in comparison with methods that use fixed number of instances from a positive image.
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Retinal oct fluid challenge. https://retouch.grand-challenge.org/home/
Solution of grt123 team. https://github.com/lfz/DSB2017/blob/master/solution-grt123-team.pdf
Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Advances in Neural Information Processing Systems, pp. 577–584 (2003)
Bendale, A., Boult, T.E.: Towards open set deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1563–1572 (2016)
Berman, S.M.: Limiting distribution of the maximum term in sequences of dependent random variables. Ann. Math. Stat. 33(3), 894–908 (1962)
Chen, M., Shi, X., Zhang, Y., Wu, D., Guizani, M.: Deep features learning for medical image analysis with convolutional autoencoder neural network. IEEE Trans. Big Data 99, 1–1 (2017)
Cheplygina, V., Sørensen, L., Tax, D.M., Pedersen, J.H., Loog, M., de Bruijne, M.: Classification of COPD with multiple instance learning. In: 22nd International Conference on Pattern Recognition (ICPR), pp. 1508–1513. IEEE (2014)
Coles, S.: An Introduction to Statistical Modeling of Extreme Values, vol. 208. Springer, Heidelberg (2001). https://doi.org/10.1007/978-1-4471-3675-0
Dou, Q., et al.: Automatic detection of cerebral microbleeds from mr images via 3D convolutional neural networks. IEEE Trans. Med. Imaging (TMI) 35(5), 1182–1195 (2016)
Ferreira, A., De Haan, L., et al.: On the block maxima method in extreme value theory: PWM estimators. Ann. Stat. 43(1), 276–298 (2015)
Hou, L., Samaras, D., Kurc, T.M., Gao, Y., Davis, J.E., Saltz, J.H.: Patch-based convolutional neural network for whole slide tissue image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2424–2433 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42(Suppl C), 60–88 (2017)
Marmor, M.F.: Mechanisms of fluid accumulation in retinal edema. In: Wolfensberger, T.J. (ed.) Macular Edema, pp. 35–45. Springer, Dordrecht (2000). https://doi.org/10.1007/978-94-011-4152-9_4
Quellec, G., Cazuguel, G., Cochener, B., Lamard, M.: Multiple-instance learning for medical image and video analysis. IEEE Rev. Biomed. Eng. 99, 1–1 (2017)
Scheirer, W.J., Rocha, A., Micheals, R.J., Boult, T.E.: Meta-recognition: the theory and practice of recognition score analysis. IEEE Trans. Pattern Anal. Mach. Intell. (T-PAMI) 33(8), 1689–1695 (2011)
Scheirer, W.J., Rocha, A., Sapkota, A., Boult, T.E.: Towards open set recognition. IEEE Trans. Pattern Anal. Mach. Intell. (T-PAMI) 35(7), 1757–1772 (2013)
Setio, A.A.A., et al.: Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging (TMI) 35(5), 1160–1169 (2016)
Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging (TMI) 35(5), 1285–1298 (2016)
Sorensen, L., Nielsen, M., Lo, P., Ashraf, H., Pedersen, J.H., De Bruijne, M.: Texture-based analysis of COPD: a data-driven approach. IEEE Trans. Med. imaging (TMI) 31(1), 70–78 (2012)
Yan, Z., et al.: Multi-instance deep learning: discover discriminative local anatomies for body part recognition. IEEE Trans. Med. Imaging (TMI) 35(5), 1332–1343 (2016)
Zhu, W., Lou, Q., Vang, Y.S., Xie, X.: Deep multi-instance networks with sparse label assignment for whole mammogram classification. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 603–611. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_69
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Tennakoon, R., Gostar, A.K., Hoseinnezhad, R., de-Bruijne, M., Bab-Hadiashar, A. (2019). Deep Multi-instance Volumetric Image Classification with Extreme Value Distributions. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11363. Springer, Cham. https://doi.org/10.1007/978-3-030-20893-6_37
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