Localisation of Colorectal Polyps by Convolutional Neural Network Features Learnt from White Light and Narrow Band Endoscopic Images of Multiple Databases

Zheng, Yali; Zhang, Ruikai; Yu, Ruoxi; Jiang, Yuqi; Mak, Tony Wing Chung; Wong, Sunny H.; Lau, James Y.; Poon, Chi‐Sang

doi:10.1109/embc.2018.8513337

Cited by 37 publications

(28 citation statements)

References 10 publications

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“…Table 3 summarizes the studies that have leveraged AI to assist in the diagnosis of large intestinal diseases, most of which focus on polyp detection, and related to identification, localization, and segmentation. Three studies of polyp segmentation showed high accuracy (57-59), while among the four studies of polyp localization (23,(60)(61)(62), there has been great heterogeneity concerning data between training and test sets, subsequently leading to the variable performance of these models. Nevertheless, the accuracy of most models has been greater than 85% (13,(63)(64)(65)(66).…”

Section: Large Intestinal Diseasesmentioning

confidence: 99%

Application of artificial intelligence in gastrointestinal disease: a narrative review

Zhou¹,

Hu²,

Huang³

et al. 2021

Ann Transl Med

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The review was carried out from two aspects of endoscopic types and gastrointestinal diseases, and briefly summarized the challenges and future directions in this field.Background: Due to the advancement of computational power and a surge of available data, a solid foundation has been laid for the growth of AI. Specifically, varied machine learning (ML) techniques have been emerging in endoscopic image analysis. To improve the accuracy and efficiency of clinicians, AI has been widely applied to gastrointestinal endoscopy.Methods: PubMed electronic database was searched using the keywords containing "AI", "ML", "deep learning (DL)", "convolution neural network", "endoscopy (such as white light endoscopy (WLE), narrow band imaging (NBI) endoscopy, magnifying endoscopy with narrow band imaging (ME-NBI), chromoendoscopy, endocytoscopy (EC), and capsule endoscopy (CE))". Search results were assessed for relevance and then used for detailed discussion. Conclusions: This review described the basic knowledge of AI, ML, and DL, and summarizes the application of AI in various endoscopes and gastrointestinal diseases. Finally, the challenges and directions of AI in clinical application were discussed. At present, the application of AI has solved some clinical problems, but more still needs to be done.

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Section: Large Intestinal Diseasesmentioning

confidence: 99%

Application of artificial intelligence in gastrointestinal disease: a narrative review

Zhou¹,

Hu²,

Huang³

et al. 2021

Ann Transl Med

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“…49 There were several recent studies that suggested a DL approach to decrease the adenoma miss rate. [50][51][52] They showed that it is possible to detect polyps in real-time with reasonable accuracy. For instance, Zhang et al achieved an 84% F1-score at speeds of 50 frames-per-second with a modified Single Shot MultiBox Detector.…”

Section: Ai-based Detection In Endoscopymentioning

confidence: 99%

“…Zheng et al proposed a real-time polyp detector based on You-Only-Look-Once in endoscopic images. 52 Another promising area for CNN technology applications is WCE. 54 It is more comfortable and less invasive than conventional endoscopy with a light tube.…”

Section: Ai-based Detection In Endoscopymentioning

confidence: 99%

Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy

Choi

Shin

Jung³

et al. 2020

Clin Endosc

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Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing to the enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithms for deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computer vision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, and challenges of deep-learning technology. clin endosc 2020;53:117-126

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“…Most of the approaches were based on handcrafted feature descriptors, including texture, color, and shape [4][5][6][7]. In recent years, deep learning approaches have been incorporated to further enhance the accuracy of detection and segmentation [8][9][10][11][12][13][14][15][16][17][18][19][20]. Since colonoscopy is the gold standard for polyp screening, more literature with colonoscopy can be found compared to wireless endoscopy.…”

Section: Introductionmentioning

confidence: 99%

“…Various CNN models were trained on 8641 internally collected colonoscopy images, and the models were analyzed through sevenfold cross-validation [19]. A regression-based YOLO (you only look once) detection model was explored for polyp localization [20] on white light and narrow-band polyp images.…”

Section: Introductionmentioning

confidence: 99%

Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps

Sornapudi

Meng

2019

Applied Sciences

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The early detection of polyps could help prevent colorectal cancer. The automated detection of polyps on the colon walls could reduce the number of false negatives that occur due to manual examination errors or polyps being hidden behind folds, and could also help doctors locate polyps from screening tests such as colonoscopy and wireless capsule endoscopy. Losing polyps may result in lesions evolving badly. In this paper, we propose a modified region-based convolutional neural network (R-CNN) by generating masks around polyps detected from still frames. The locations of the polyps in the image are marked, which assists the doctors examining the polyps. The features from the polyp images are extracted using pre-trained Resnet-50 and Resnet-101 models through feature extraction and fine-tuning techniques. Various publicly available polyp datasets are analyzed with various pertained weights. It is interesting to notice that fine-tuning with balloon data (polyp-like natural images) improved the polyp detection rate. The optimum CNN models on colonoscopy datasets including CVC-ColonDB, CVC-PolypHD, and ETIS-Larib produced values (F1 score, F2 score) of (90.73, 91.27), (80.65, 79.11), and (76.43, 78.70) respectively. The best model on the wireless capsule endoscopy dataset gave a performance of (96.67, 96.10). The experimental results indicate the better localization of polyps compared to recent traditional and deep learning methods.

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Localisation of Colorectal Polyps by Convolutional Neural Network Features Learnt from White Light and Narrow Band Endoscopic Images of Multiple Databases

Cited by 37 publications

References 10 publications

Application of artificial intelligence in gastrointestinal disease: a narrative review

Application of artificial intelligence in gastrointestinal disease: a narrative review

Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy

Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps

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