Abstract
Deep learning has been a growing trend in various fields of natural image classification as it performs state-of-the-art result on several challenging tasks. Despite its success, deep learning applied to medical image analysis has not been wholly explored. In this paper, we study on convolutional neural network (CNN) architectures applied to a Bosniak classification problem to classify Computed Tomography images into five Bosniak classes. We use a new medical image dataset called as the Bosniak classification dataset which will be fully introduced in this paper. For this data set, we employ a multi-phase CNN approach to predict classification accuracy. We also discuss the representation power of CNN compared to previously developed features (Garbor features) in medical image. In our experiment, we use data combination method to enlarge the data set to avoid overfitting problem in multi-phase medical imaging system. Using multi-phase CNN and data combination method we proposed, we have achieved 48.9 % accuracy on our test set, which improves the hand-crafted features by 11.9 %.
This work was supported by Brain Fusion Program of Soul National University (800-20140265).
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- 1.
Digital image and Communications in Medicine (DICOM) is a standard for handling, storing, printing, and transmitting information in medical image.
- 2.
The term ‘unregistered’ is used to indicate that the three images from different phases have different shapes and sizes of lesions.
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Lee, M., Lee, H., Oh, J., Lee, H.J., Kim, S.H., Kwak, N. (2016). Unregistered Bosniak Classification with Multi-phase Convolutional Neural Networks. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_3
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