Abstract
Rapid and accurate classification of medical images plays an important role in medical diagnosis. Nowadays, for medical image classification, there are some methods based on machine learning, deep learning and transfer learning. However, these methods may be time-consuming and not suitable for small datasets. Based on these limitations, we propose a novel method which combines few-shot learning method and attention mechanism. Our method takes end-to-end learning to solve the problem of artificial feature extraction in machine learning and few-shot learning method is especially to fulfill small datasets tasks, which means it performs better than traditional deep learning. In addition, our method can make full use of spatial and channel information which enhances the representation of models. Furthermore, we adopt 1 \(\times \) 1 convolution to enhance the interactions of cross channel information. Then we apply the model to the medical dataset Brain Tumor and compare it with the transfer learning method and Dual Path Network. Our method achieves an accuracy of 92.44%, which is better than the above methods.
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Acknowledgments
We thank all viewers who provided the thoughtful and constructive comments on this paper. This research is funded by Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China. The computation is supported by ECNU Multifunctional Platform for Innovation (001).
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Cai, A., Hu, W., Zheng, J. (2020). Few-Shot Learning for Medical Image Classification. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12396. Springer, Cham. https://doi.org/10.1007/978-3-030-61609-0_35
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