Abstract
Automatic detection of liver lesions in CT images poses a great challenge for researchers. In this work we present a deep learning approach that models explicitly the variability within the non-lesion class, based on prior knowledge of the data, to support an automated lesion detection system. A multi-class convolutional neural network (CNN) is proposed to categorize input image patches into sub-categories of boundary and interior patches, the decisions of which are fused to reach a binary lesion vs non-lesion decision. For validation of our system, we use CT images of 132 livers and 498 lesions. Our approach shows highly improved detection results that outperform the state-of-the-art fully convolutional network. Automated computerized tools, as shown in this work, have the potential in the future to support the radiologists towards improved detection.
M. Frid-Adar and I. Diamant—Equal Contributors
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Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H. (2017). Modeling the Intra-class Variability for Liver Lesion Detection Using a Multi-class Patch-Based CNN. In: Wu, G., Munsell, B., Zhan, Y., Bai, W., Sanroma, G., Coupé, P. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2017. Lecture Notes in Computer Science(), vol 10530. Springer, Cham. https://doi.org/10.1007/978-3-319-67434-6_15
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DOI: https://doi.org/10.1007/978-3-319-67434-6_15
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