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https://hdl.handle.net/2440/126581
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Type: | Conference paper |
Title: | Photoshopping colonoscopy video frames |
Author: | Liu, Y. Tian, Y. Maicas Suso, G. Zorron Cheng Tao Pu, L. Singh, R. Verjans, J.W. Carneiro, G. |
Citation: | Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2020, vol.2020-April, pp.1-5 |
Publisher: | IEEE |
Issue Date: | 2020 |
Series/Report no.: | IEEE International Symposium on Biomedical Imaging |
ISBN: | 9781538693308 |
ISSN: | 1945-7928 1945-8452 |
Conference Name: | IEEE International Symposium on Biomedical Imaging (ISBI) (3 Apr 2020 - 7 Apr 2020 : Iowa City, Iowa, USA) |
Statement of Responsibility: | Yuyuan Liu, Yu Tian, Gabriel Maicas, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro |
Abstract: | The automatic detection of frames containing polyps from a colonoscopy video sequence is an important first step for a fully automated colonoscopy analysis tool. Typically, such detection system is built using a large annotated data set of frames with and without polyps, which is expensive to be obtained. In this paper, we introduce a new system that detects frames containing polyps as anomalies from a distribution of frames from exams that do not contain any polyps. The system is trained using a one-class training set consisting of colonoscopy frames without polyps – such training set is considerably less expensive to obtain, compared to the 2-class data set mentioned above. During inference, the system is only able to reconstruct frames without polyps, and when it tries to reconstruct a frame with polyp, it automatically removes (i.e., photoshop) it from the frame – the difference between the input and reconstructed frames is used to detect frames with polyps. We name our proposed model as anomaly detection generative adversarial network (ADGAN), comprising a dual GAN with two generators and two discriminators. To test our framework, we use a new colonoscopy data set with 14317 images, split as a training set with 13350 images without polyps, and a testing set with 290 abnormal images containing polyps and 677 normal images without polyps. We show that our proposed approach achieves the state-of-the-art result on this data set, compared with recently proposed anomaly detection systems. |
Keywords: | Deep learning; anomaly detection; oneclass classification; adversarial learning |
Rights: | ©2020 IEEE |
DOI: | 10.1109/ISBI45749.2020.9098406 |
Grant ID: | http://purl.org/au-research/grants/arc/DP180103232 |
Published version: | https://ieeexplore.ieee.org/xpl/conhome/9091448/proceeding |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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