Abstract
In the medical domain, one challenge of deep learning is to build sample-efficient models from a small number of labeled data. In recent years, meta-learning has become an important approach to few-shot image classification. However, current research on meta-learning focuses on learning from a few examples; we propose to extend few-shot learning to medium-shot to evaluate medical classification tasks in a more realistic setup. We build a baseline evaluation procedure by analyzing two representative meta-learning methods through the lens of bias-variance tradeoff, and propose to fuse the two techniques for better bias-variance equilibrium. The proposed method, Task Adaptive Metric Space (TAMS), fine-tunes parameters of a metric space to represent medical data in a more semantically meaningful way. Our empirical studies suggest that TAMS outperforms other baselines. Visualizations on the metric space show TAMS leads to better-separated clusters. Our baselines and evaluation procedure of the proposed TAMS opens the door to more research on medium-shot medical image classification.
X. Jiang, L. Ding—Equal contribution.
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References
Bengio, S., Bengio, Y., Cloutier, J., Gecsei, J.: On the optimization of a synaptic learning rule. In: Preprints Conference on Optimality in Artificial and Biological Neural Networks, pp. 6–8. University of Texas (1992)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126–1135 (2017)
Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning. Springer Series in Statistics, vol. 1. Springer, New York (2001). https://doi.org/10.1007/978-0-387-21606-5
Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–1131 (2018)
Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2 (2015)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Maicas, G., Bradley, A.P., Nascimento, J.C., Reid, I., Carneiro, G.: Training medical image analysis systems like radiologists. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 546–554. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_62
Mitchell, T.M., Thrun, S.B.: Explanation-based neural network learning for robot control. In: Advances in Neural Information Processing Systems, pp. 287–294 (1993)
Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning (2016)
Schmidhuber, J.: Evolutionary principles in self-referential learning. On learning now to learn: the meta-meta-meta...-hook. Diploma thesis, Technische Universitat Munchen, Germany, 14 May 1987. http://www.idsia.ch/~juergen/diploma.html
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, pp. 815–823 (2015)
Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. CoRR abs/1703.05175 (2017). arXiv:1703.05175
Vilalta, R., Drissi, Y.: A perspective view and survey of meta-learning. Artif. Intell. Rev. 18(2), 77–95 (2002)
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, pp. 3630–3638 (2016)
Acknowledgement
The authors thank Tanya Nair, Martine Bertrand and the Imagia team for their support. XJ acknowledges the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research.
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Jiang, X., Ding, L., Havaei, M., Jesson, A., Matwin, S. (2019). Task Adaptive Metric Space for Medium-Shot Medical Image Classification. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_17
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DOI: https://doi.org/10.1007/978-3-030-32239-7_17
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