Massive Colonoscopy Images Oriented Polyp Detection

Since more than 90% of colorectal cancers are converted from colorectal polyps, colonoscopy is the most effective method for early detection of colorectal polyps. However, artificial polyp judgement leads to a high missed diagnosis rate during colonoscopy inspection. To reduce the missed diagnosis rate, we propose an end-to-end deep learning based polyp detection method combining a series of pretreatment methods with a multiple classification based detection network. We have compared our method with several currently popular object detection methods. Experiment results show that our method has effective improvements on detection precision and performance.

[1]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[3]  Shifeng Zhang,et al.  Single-Shot Refinement Neural Network for Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Kaiming He,et al.  Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[5]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[6]  Sun Young Park,et al.  A Colon Video Analysis Framework for Polyp Detection , 2012, IEEE Transactions on Biomedical Engineering.

[7]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[8]  Fernando Vilariño,et al.  Towards automatic polyp detection with a polyp appearance model , 2012, Pattern Recognit..

[9]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Dimitris A. Karras,et al.  Computer-aided tumor detection in endoscopic video using color wavelet features , 2003, IEEE Transactions on Information Technology in Biomedicine.

[12]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[13]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Jung-Hwan Oh,et al.  Polyp Detection in Colonoscopy Video using Elliptical Shape Feature , 2007, 2007 IEEE International Conference on Image Processing.

[15]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Jung-Hwan Oh,et al.  Part-Based Multiderivative Edge Cross-Sectional Profiles for Polyp Detection in Colonoscopy , 2014, IEEE Journal of Biomedical and Health Informatics.

[17]  Hao Chen,et al.  Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos , 2017, IEEE Journal of Biomedical and Health Informatics.

[18]  Nima Tajbakhsh,et al.  Automatic polyp detection in colonoscopy videos , 2017, Medical Imaging.