Abstract
This article designs a cascaded neural network to diagnose colonoscopic images automatically. With the limited number of labeled polyps masked in binary, the proposed detection network uses a hetero-encoder to map a colonoscopic image to an aggregated set of exemplified images as data argumentation to force the successive autoencoder to learn important features acting as a denoising autoencoder. In other words, the autoencoder denoises the transient images generated in the precedent hetero-encoder training process by auto-associating the ground truth and its variants. A hard attention model classifies the segmented image and applies a local region proposal network (RPN) to the generation and aggression of bounding boxes only on the segmented images to allow a more precise detection such that computations on bounding boxes with less information are avoided. The proposed system can outperform current complex state-of-art methods like faster-R-CNN from the experiments on endoscopic images.
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References
Al-Mnayyis A et al (2020) Lumbar disk 3D modeling from a limited number of MRI axial slices. Int J Electr Comput Eng 10–4:4101–4108
AlZu’bi S et al (2020) Parallel implementation for 3D medical volume fuzzy segmentation. Pattern Recognit Lett 130:312–318
Coimbra MT, Cunha JPS (2006) Mpeg -7 visual descriptors contributions for automated feature extraction in capsule endoscopy. IEEE Trans Circuits Syst Video Technol 16:628–637
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of advances in neural information processing systems, Lake Tahoe, Nevada
Dai J, Li Y, He K, Sun J (2016) R-FCN: object detection via region-based fully convolutional networks. In: Advances in neural information processing systems, pp 379–387
Hwang M et al (2020) An automated detection system for colonoscopy images using a dual encoder–decoder model. Comput Med Imaging Graph 84(101763):1–9
Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A, Bottou L (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(12):3371–3408
Mnih V, Heess N, Graves A (2014) Recurrent models of visual attention. In: Advances in neural information processing systems, pp 2204–2212
Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99
Goodfellow I et al (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680
Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587
Girshick R (2015) Fast R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448
Fernandez-Esparrach G et al (2016) Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps. Endoscopy 48(09):837–842
VázquezD et al (2017) A benchmark for endoluminal scene segmentation of colonoscopy images. J Healthc Eng. arXiv preprint arXiv:1612.00799
Zhao J, Mathieu M, LeCun Y (2016) Energy-based generative adversarial network. arXiv preprint arXiv:1609.03126
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: ICRL
Phung VH, Rhee EJ (2019) A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Appl Sci 9(21):4500
Bradski G, Kaehler A (2008) Learning OpenCV: computer vision with the Opencv library. O’Reilly Media, Inc., Sebastopol
Sutton RS, Barto AG (2018) Reinforcement learning: an introduction, 2nd edn. MIT Press, Cambridge
Sutton RS, McAllester DA, Singh SP, Mansour Y (2000) Policy gradient methods for reinforcement learning with function approximation. In: Advances in neural information processing systems, pp 1057–1063
Vázquez D et al (2017) A benchmark for endoluminal scene segmentation of colonoscopy images. J Healthc Eng 2017
Bernal J et al (2015) WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput Med Imaging Graph 43, pp 99–111
Silva J et al (2013) Towards embedded detection of polyps in video colonoscopy and WCE images for early diagnosis of colorectal cancer. Int J Comput Assist Radiol Surg 9:283–293
Arlot S, Celisse A (2010) A survey of cross-validation procedures for model selection. Stat Surv 4:40–79
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Proceedings of the international conference on medical image computing and computer-assisted intervention, pp 234–241
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
Funding
This study was funded by the National Key R&D Program of China (2017YFC0908200), the Key Technology Research and Development Program of Zhejiang Province (no. 2017C03017), and the Key Project of Yiwu Science and Technology plan, China. no. 20-3-067.
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Hwang, M., Qian, Y., Wu, C. et al. A local region proposals approach to instance segmentation for intestinal polyp detection. Int. J. Mach. Learn. & Cyber. 14, 1591–1603 (2023). https://doi.org/10.1007/s13042-022-01714-4
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DOI: https://doi.org/10.1007/s13042-022-01714-4