Abstract
Focal liver lesion classification is important to the diagnostics of liver disease. In clinics, lesion type is usually determined by multi-phase contrast-enhanced CT images. Previous methods of automatic liver lesion classification are conducted on lesion-level, which rely heavily on lesion-level annotations. In order to reduce the burden of annotation, in this paper, we explore automatic liver lesion classification with weakly-labeled CT images (i.e. with only image-level labels). The major challenge is how to localize the region of interests (ROIs) accurately by using only coarse image-level annotations and accordingly make the right lesion classification decision. We propose a cascade attention network to address the challenge by two stages: Firstly, a dual-attention dilated residual network (DADRN) is proposed to generate a class-specific lesion localization map, which incorporates spatial attention and channel attention blocks for capturing the high-level feature map’s long-range dependencies and helps to synthesize a more semantic-consistent feature map, and thereby boosting weakly-supervised lesion localization and classification performance; Secondly, a multi-channel dilated residual network (MCDRN) embedded with a convolutional long short-term memory (CLSTM) block is proposed to extract temporal enhancement information and make the final classification decision. The experiment results show that our method could achieve a mean classification accuracy of 89.68%, which significantly mitigates the performance gap between weakly-supervised approaches and fully supervised counterparts.
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Acknowledgements
This work was supported in part by Major Scientific Research Project of Zhejiang Lab under the Grant No. 2018DG0ZX01, in part by the Science and Technology Support Program of Hangzhou under the Grant No. 20172011A038, and in part by the Grant-in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant No. 18H03267.
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Chen, X. et al. (2019). A Cascade Attention Network for Liver Lesion Classification in Weakly-Labeled Multi-phase CT Images. In: Wang, Q., et al. Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. DART MIL3ID 2019 2019. Lecture Notes in Computer Science(), vol 11795. Springer, Cham. https://doi.org/10.1007/978-3-030-33391-1_15
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DOI: https://doi.org/10.1007/978-3-030-33391-1_15
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