Abstract
This chapter proposes a method to detect metastatic liver cancer from X-ray CT images using a convolutional neural network (CNN). The proposed method generates various lesion images by the combination of three kinds of generation methods: (1) synthesis using Poisson Blending, (2) generation based on CT value distributions, and (3) generation using deep convolutional generative adversarial networks (DCGANs). The proposed method constructs two kinds of detectors by using synthetic (fake) lesion images generated by the methods as well as real ones. One of the detectors is a 2D CNN for detecting candidate regions in a CT image, and the other is a 3D CNN for validating the candidate regions. Experimental results showed that the proposed method gave 0.30 improvement from 0.65 to 0.95 in terms of the detection rate, and 0.70 improvement from 0.90 to 0.20 in terms of the number of false detections per case. From the results, we confirmed the effectiveness of the proposed method.
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Doman, K., Konishi, T., Mekada, Y. (2020). Lesion Image Synthesis Using DCGANs for Metastatic Liver Cancer Detection. In: Lee, G., Fujita, H. (eds) Deep Learning in Medical Image Analysis . Advances in Experimental Medicine and Biology, vol 1213. Springer, Cham. https://doi.org/10.1007/978-3-030-33128-3_6
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DOI: https://doi.org/10.1007/978-3-030-33128-3_6
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