Automatic Polyp Recognition in Colonoscopy Images Using Deep Learning and Two-Stage Pyramidal Feature Prediction

Xiao, Jia; Mai, Xiaochun; Cui, Yi; Yuan, Yixuan; Xing, Xiaohan; Seo, Hyunseok; Liu, Xing; Meng, Max Q.-H.

doi:10.1109/tase.2020.2964827

Cited by 55 publications

(30 citation statements)

References 34 publications

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“…In addition, 5 articles were obtained after screening the published articles of related systematic reviews and meta-analysis, which totaled 714 records. After 284 articles were excluded as duplicates and 372 articles were excluded on the basis of titles and abstracts, 15 studies on polyps detection (9,12,13,(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31), 10 studies on polyps classification (32)(33)(34)(35)(36)(37)(38)(39)(40)(41), and 1 (42) which was a combination of both were identified as being appropriate for full-text review. The process of literature screening and inclusion is shown in Figure 1.…”

Section: Literature Screeningmentioning

confidence: 99%

“…Studies in this systematic review included 15 preclinical studies on polyps detection (9,12,13,(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31), 10 preclinical studies on polyps classification (32)(33)(34)(35)(36)(37)(38)(39)(40)(41), and 1 (42) which was a combination of both. In terms of the detection of polyps, 5 studies exploring the (9,23,26-28) the performance of realtime AI-assisted detection reported no TN.…”

Section: The Basic Characteristics Of the Included Literaturementioning

confidence: 99%

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Artificial intelligence-assisted detection and classification of colorectal polyps under colonoscopy: a systematic review and meta-analysis

Wang¹,

Mo²,

Zhong³

et al. 2021

Ann Transl Med

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Background: Artificial intelligence (AI) is used to solve the problem of missed diagnosis of polyps in colonoscopy, which has been proved to improve the detection rate of adenomas. The aim of this review was to evaluate the diagnostic performance of AI-assisted detection and classification of polyps in colonoscopy. Methods:The literature search was undertaken on 4 electronic databases (PubMed, Web of Science, Embase, and Cochrane Library). The inclusion criteria were as follows: studies reporting AI-assisted detection and classification of polyps; studies containing patients, images, or videos receiving AI-assisted diagnosis; studies which included AI-assisted diagnosis and reported classification based on histopathology; and studies providing accurate diagnostic data. Non-English language studies, case-reports, reviews, meeting abstracts and so on were excluded. The Quality Assessment of Diagnostic Accuracy Studies-2 scale was used to evaluate the quality of literature and the Stata 13.0 software was used to perform meta-analysis.Results: Twenty-six articles were included with all of medium quality. Meta-analysis showed none of literature had any obvious publication bias. The application of AI in detection of colorectal polyps achieved a sensitivity of 0.95 [95% confidence interval (CI): 0.89-0.98] and an area under the curve (AUC) of 0.79 (95% CI: 0.79-0.82). In the AI-assisted classification, the sensitivity was 0.92 (95% CI: 0.88-0.95) with a specificity of 0.82 (95% CI: 0.71-0.89) and an AUC of 0.94 (95% CI: 0.92-0.96). For the classification of diminutive polyps, the AI-assisted technique yielded a sensitivity of 0.95 (95% CI: 0.94-0.97), a specificity of 0.88 (95% CI: 0.74-0.95), and an AUC of 0.97 (95% CI: 0.95-0.98). For AI-assisted classification under magnifying endoscopy, the sensitivity was 0.954 (95% CI: 0.92-0.96) with a specificity of 0.95 (95% CI: 0.80-0.99) and an AUC of 0.97 (95% CI: 0.95-0.98).Discussion: The AI-assisted technique demonstrates impressive accuracy for the detection and characterization of colorectal polyps and can be expected to be a novel auxiliary diagnosis method. Our study has inevitable limitations including heterogeneity due to different AI systems and the inability to further analyze the specificity and sensitivity of AI for different types of endoscopes.

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Section: Literature Screeningmentioning

confidence: 99%

Section: The Basic Characteristics Of the Included Literaturementioning

confidence: 99%

Artificial intelligence-assisted detection and classification of colorectal polyps under colonoscopy: a systematic review and meta-analysis

Wang¹,

Mo²,

Zhong³

et al. 2021

Ann Transl Med

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“…Two-stage detection/segmentation pipelines, in which a first an object detector roughly locates a polyp and then an encoder-decoder CNN delineates its margins have also been explored, e.g. in [21]. Alternative loss function have also been studied, e.g.…”

Section: Introductionmentioning

confidence: 99%

Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation

Galdrán¹,

Carneiro²,

Ballester³

2021

Pattern Recognition. ICPR International Workshops and Challenges

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Polyps represent an early sign of the development of Colorectal Cancer. The standard procedure for their detection consists of colonoscopic examination of the gastrointestinal tract. However, the wide range of polyp shapes and visual appearances, as well as the reduced quality of this image modality, turn their automatic identification and segmentation with computational tools into a challenging computer vision task. In this work, we present a new strategy for the delineation of gastrointestinal polyps from endoscopic images based on a direct extension of common encoder-decoder networks for semantic segmentation. In our approach, two pretrained encoder-decoder networks are sequentially stacked: the second network takes as input the concatenation of the original frame and the initial prediction generated by the first network, which acts as an attention mechanism enabling the second network to focus on interesting areas within the image, thereby improving the quality of its predictions. Quantitative evaluation carried out on several polyp segmentation databases shows that double encoder-decoder networks clearly outperform their single encoder-decoder counterparts in all cases. In addition, our best double encoder-decoder combination attains excellent segmentation accuracy and reaches state-of-the-art performance results in all the considered datasets, with a remarkable boost of accuracy on images extracted from datasets not used for training.

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“…Colon polyp segmentation is considered a challenging task as a polyp could have various forms and does not have clear borders in some cases [1]. Furthermore, the lack of large and representative datasets in the endoscopy domain is a persistent challenge that still exists today.…”

Section: Introductionmentioning

confidence: 99%

Employing GRU to combine feature maps in DeeplabV3 for a better segmentation model

Haithami

Ahmed

Liao

et al. 2021

NMI

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In this paper, we aim to enhance the segmentation capabilities of DeeplabV3 by employing Gated Recurrent Neural Network (GRU). A 1-by-1 convolution in DeeplabV3 was replaced by GRU after the Atrous Spatial Pyramid Pooling (ASSP) layer to combine the input feature maps. The convolution and GRU have sharable parameters, though, the latter has gates that enable/disable the contribution of each input feature map. The experiments on unseen test sets demonstrate that employing GRU instead of convolution would produce better segmentation results. The used datasets are public datasets provided by MedAI competition.

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Automatic Polyp Recognition in Colonoscopy Images Using Deep Learning and Two-Stage Pyramidal Feature Prediction

Cited by 55 publications

References 34 publications

Artificial intelligence-assisted detection and classification of colorectal polyps under colonoscopy: a systematic review and meta-analysis

Artificial intelligence-assisted detection and classification of colorectal polyps under colonoscopy: a systematic review and meta-analysis

Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation

Employing GRU to combine feature maps in DeeplabV3 for a better segmentation model

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