Automated detection of lesion in computer tomography images based on Cascade R-CNN

Ran Liu; Yang Zhao; Yaqiong Liu; Xi Chen; Shanshan Cui; Feifei Wang; Lin Yi

doi:10.1117/12.2579644

10 November 2020 Automated detection of lesion in computer tomography images based on Cascade R-CNN

Ran Liu, Yang Zhao, Yaqiong Liu, Xi Chen, Shanshan Cui, Feifei Wang, Lin Yi

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 1158416 (2020) https://doi.org/10.1117/12.2579644
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

Computed tomography (CT) images-based early disease screening is of high significance for the detection of the occurrence of cancer. It has been demonstrated that lesion detection using deep learning in CT images are significantly effective for the early stage of cancer. In this study, an improved Cascade R-CNN is proposed. In the proposed network, the Feature Pyramid Network (FPN) is introduced to complete automatic computer-aided lesion detection. Based on this trick, numerous tiny lesions in CT images can be well detected. Experimental results on the DeepLesion show that the proposed method can achieve the mAP of 0.598 with a threshold of 0.5 and 85.2% sensitivity with 4 false positives per image.

Citation Download Citation

Ran Liu, Yang Zhao, Yaqiong Liu, Xi Chen, Shanshan Cui, Feifei Wang, and Lin Yi "Automated detection of lesion in computer tomography images based on Cascade R-CNN", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 1158416 (10 November 2020); https://doi.org/10.1117/12.2579644

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