Abstract
With regard to pulmonary nodule detection, due to the similar texture and shape as particular tissues, it is difficult for Computer-Aided Detection (CAD) system in detecting pulmonary nodule with both high accuracy and sensitivity. To address this problem, we design a 3D automated pulmonary nodule detection where a auxiliary 3D generative adversarial network is embedded. This well-trained auxiliary component that fully learns volumetrically contextual information of nodule and non-nodule structure, is exploited for each input sample of detection model to generate a derivative which only preserve background context by removing all the nodules. By learning the feature contrast between each input and its derivative, our detection model achieves competitive performance to state-of-the-art approaches for the pulmonary nodule detection task.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China [Grant numbers 61672386]; the Anhui Provincial Natural Science Foundation of China [Grant numbers 1708085MF142]; the Major Research Project Breeding Foundation of Wannan Medical College [Grant numbers WK2017Z01]; ANHUI Province Key Laboratory of Affective Computing and Advanced Intelligent Machine [Grant numbers ACAIM180202]; the Anhui Provincial Humanities and Social Science Foundation of China [Grant numbers SK2018A0198].
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Chang, J., Ye, M., Gu, N., Zhang, X., Lin, C., Ye, H. (2019). Automatical Pulmonary Nodule Detection by Feature Contrast Learning. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_5
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DOI: https://doi.org/10.1007/978-3-030-26763-6_5
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