Abstract
Deep Learning provides exciting solutions to problems in medical image analysis and is regarded as a key method for future applications. However, only a few annotated medical image datasets exist compared to numerous natural images. A solution to this problem is transfer learning using ImageNet. However, because the domain of ImageNet is different from that of medical images, the results of transfer learning are not always good. Therefore, we propose a model to investigate transfer learning by self-supervised learning using medical images. It is widely known that the results of Computerized Tomography (CT) scan are 3D volume images. There are lots of slices in CT or Magnetic Resonance Imaging scan images. So why not make these slices to a class? It is imperative to formulate this intuition as a self-supervised feature learning at the case-level. The results of our experiment demonstrated that, under self-supervised feature learning settings, our method surpasses the transfer learning method that uses ImageNet for classification. By experimenting with unannotated datasets, our method is remarkable for consistently improving test performance with a few annotated data. By fine-tuning the learned features, we obtained competitive results for self-supervised learning and classification tasks.
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Acknowledgements
We would like to thank Sir Run Run Shaw Hospital for providing medical data and helpful advice on this research. This work is supported in part by the Grantin Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant No. 20KK0234, No. 20K21821, No.18H03267, in part by Zhejiang Lab Program under the Grant No. 2020ND8AD01.
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Dong, H. et al. (2021). Case Discrimination: Self-supervised Feature Learning for the Classification of Focal Liver Lesions. In: Chen, YW., Tanaka, S., Howlett, R.J., Jain, L.C. (eds) Innovation in Medicine and Healthcare. Smart Innovation, Systems and Technologies, vol 242. Springer, Singapore. https://doi.org/10.1007/978-981-16-3013-2_20
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DOI: https://doi.org/10.1007/978-981-16-3013-2_20
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