Abstract
Human-level diagnostic performance from intelligent systems often depends on large set of training data. However, the amount of available data for model training may be limited for part of diseases, which would cause the widely adopted deep learning models not generalizing well. One alternative simple approach to small class prediction is the traditional k-nearest neighbor (kNN). However, due to the non-parametric characteristics of kNN, it is difficult to combine the kNN classification into the learning of feature extractor. This paper proposes an end-to-end learning strategy to unify the kNN classification and the feature extraction procedure. The basic idea is to enforce that each training sample and its K nearest neighbors belong to the same class during learning the feature extractor. Experiments on multiple small-class and class-imbalanced medical image datasets showed that the proposed deep kNN outperforms both kNN and other strong classifiers.
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References
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
He, K., Girshick, R., Dollr, P.: Rethinking imageNet pre-training. In: CVPR, pp. 4918–4927(2019)
Li, A., et al.: Large-scale few-shot learning: Knowledge transfer with class hierarchy. In: CVPR, pp. 7212–7220 (2019)
Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: KDD, pp. 245–250 (2001)
Turk, M., Pentland, A.: Face recognition using eigenfaces. In: CVPR, pp. 586–587 (1991)
Zhang, H., et al.: SVM-KNN: discriminative nearest neighbor classification for visual category recognition. In: CVPR, pp. 2126–2136 (2006)
Koniusz, P., et al.: Higher-order occurrence pooling for bags-of-words: visual concept detection. IEEE Trans. Pattern Anal. Mach. Intell. 39(2), 313–326 (2016)
Yosinski, J., et al.: How transferable are features in deep neural networks? In: NeurIPS, pp. 3320–3328 (2014)
Goldberger, J., et al.: Neighbourhood components analysis. In NeurIPS, pp. 513–520 (2005)
Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10(2), 207–244 (2009)
Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv:1703.07737 (2017)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR, pp. 815–823 (2015)
Gordo, A., et al.: End-to-end learning of deep visual representations for image retrieval. IJCV 124(2), 237–254 (2017)
Sun, X., Yang, J., Sun, M., Wang, K.: A benchmark for automatic visual classification of clinical skin disease images. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_13
Codella, N.C.F., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: ISBI, pp. 168–172 (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
He, K., et al. Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Zhuang, J., et al.: Care: class attention to regions of lesion for classification on imbalanced data. In: MIDL, pp. 588–597 (2019)
Acknowledgement
This work is supported in part by the National Key Research and Development Program (grant No. 2018YFC1315402), the Guangdong Key Research and Development Program (grant No. 2019B020228001), the National Natural Science Foundation of China (grant No. U1811461), and the Guangzhou Science and Technology Program (grant No. 201904010260).
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Zhuang, J., Cai, J., Wang, R., Zhang, J., Zheng, WS. (2020). Deep kNN for Medical Image Classification. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12261. Springer, Cham. https://doi.org/10.1007/978-3-030-59710-8_13
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DOI: https://doi.org/10.1007/978-3-030-59710-8_13
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