AFP-Net: Realtime Anchor-Free Polyp Detection in Colonoscopy

Colorectal cancer (CRC) is a common and lethal disease. Globally, CRC is the third most commonly diagnosed cancer in males and the second in females. For colorectal cancer, the best screening test available is the colonoscopy. During a colonoscopic procedure, a tiny camera at the tip of the endoscope generates a video of the internal mucosa of the colon. The video data are displayed on a monitor for the physician to examine the lining of the entire colon and check for colorectal polyps. Detection and removal of colorectal polyps are associated with a reduction in mortality from colorectal cancer. However, the miss rate of polyp detection during colonoscopy procedure is often high even for very experienced physicians. The reason lies in the high variation of polyp in terms of shape, size, textural, color and illumination. Though challenging, with the great advances in object detection techniques, automated polyp detection still demonstrates a great potential in reducing the false negative rate while maintaining a high precision. In this paper, we propose a novel anchor free polyp detector that can localize polyps without using predefined anchor boxes. To further strengthen the model, we leverage a Context Enhancement Module and Cosine Ground truth Projection. Our approach can respond in real time while achieving state-of-the-art performance with 99.36% precision and 96.44% recall.

[1]  A. Jemal,et al.  Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.

[2]  Carmen C. Y. Poon,et al.  Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker , 2018, Pattern Recognit..

[3]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  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.

[5]  Fernando Vilariño,et al.  WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians , 2015, Comput. Medical Imaging Graph..

[6]  Ning Zhang,et al.  Improved Multimodal Representation Learning with Skip Connections , 2017, ACM Multimedia.

[7]  Nima Tajbakhsh,et al.  Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information , 2016, IEEE Transactions on Medical Imaging.

[8]  Yu Cao,et al.  HeteroSpark: A heterogeneous CPU/GPU Spark platform for machine learning algorithms , 2015, 2015 IEEE International Conference on Networking, Architecture and Storage (NAS).

[9]  Yu Cao,et al.  People Re-Identification by Multi-Branch CNN with Multi-Scale Features , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[10]  Xingyi Zhou,et al.  Objects as Points , 2019, ArXiv.

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

[12]  Andreas Holzinger,et al.  Biomedical image augmentation using Augmentor , 2019, Bioinform..

[13]  Ilangko Balasingham,et al.  Polyp Detection and Segmentation using Mask R-CNN: Does a Deeper Feature Extractor CNN Always Perform Better? , 2019, 2019 13th International Symposium on Medical Information and Communication Technology (ISMICT).

[14]  Hei Law,et al.  CornerNet: Detecting Objects as Paired Keypoints , 2018, ECCV.

[15]  Qi Tian,et al.  CenterNet: Keypoint Triplets for Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[16]  Ning Zhang,et al.  iHear Food: Eating Detection Using Commodity Bluetooth Headsets , 2016, 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE).

[17]  Aymeric Histace,et al.  Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge , 2017, IEEE Transactions on Medical Imaging.

[18]  Aymeric Histace,et al.  Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer , 2014, International Journal of Computer Assisted Radiology and Surgery.

[19]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[20]  Chenxi Zhang,et al.  3D Anchor-Free Lesion Detector on Computed Tomography Scans , 2019, ArXiv.

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

[22]  Quan Wang,et al.  An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

[23]  Dan Zecha,et al.  A closer look: Small object detection in faster R-CNN , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).

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

[25]  Jian Yang,et al.  DSFD: Dual Shot Face Detector , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Ilangko Balasingham,et al.  Automatic Colon Polyp Detection Using Region Based Deep CNN and Post Learning Approaches , 2018, IEEE Access.

[27]  Marius Pedersen,et al.  Y-Net: A deep Convolutional Neural Network for Polyp Detection , 2018, BMVC.

[28]  Fernando Vilariño,et al.  Texture-Based Polyp Detection in Colonoscopy , 2009, Bildverarbeitung für die Medizin.

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

[30]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Yuning Jiang,et al.  FoveaBox: Beyound Anchor-Based Object Detection , 2019, IEEE Transactions on Image Processing.

[32]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Larry S. Davis,et al.  SSH: Single Stage Headless Face Detector , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[34]  Kai Chen,et al.  Region Proposal by Guided Anchoring , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[36]  Chang Liu,et al.  Improving tuberculosis diagnostics using deep learning and mobile health technologies among resource-poor communities in Perú , 2017 .

[37]  Ning Zhang,et al.  Improving Tuberculosis Diagnostics Using Deep Learning and Mobile Health Technologies among Resource-Poor and Marginalized Communities , 2016, 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE).

[38]  A. M. Leufkens,et al.  Factors influencing the miss rate of polyps in a back-to-back colonoscopy study , 2012, Endoscopy.

[39]  Frank Meng,et al.  Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps , 2019, Applied Sciences.

[40]  Hao Chen,et al.  FCOS: Fully Convolutional One-Stage Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[41]  A. Jemal,et al.  Colorectal cancer statistics, 2017 , 2017, CA: a cancer journal for clinicians.

[42]  Wei Li,et al.  Stochastic Gradient Descent with Hyperbolic-Tangent Decay , 2018, ArXiv.

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