2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2018
DOI: 10.1109/embc.2018.8513274
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Automatic detection of early gastric cancer in endoscopic images using a transferring convolutional neural network

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Cited by 101 publications
(69 citation statements)
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“…All 4245 studies were screened and 106 full-length articles and/or abstracts were assessed. Nineteen studies 2 5 6 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 reported on the detection and/ or classification of gastrointestinal neoplastic lesions by CNN. Among the 19 studies, five 6 12 15 17 26 reported on efficacy of CNN in diagnosing esophageal neoplasia, eight 5 14 16 18 19 22 23 25 reported on use of CNN in neoplasia of the stomach and six 2 11 13 20 21 24 evaluated use of CNN in diagnosing colorectal neoplasia.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…All 4245 studies were screened and 106 full-length articles and/or abstracts were assessed. Nineteen studies 2 5 6 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 reported on the detection and/ or classification of gastrointestinal neoplastic lesions by CNN. Among the 19 studies, five 6 12 15 17 26 reported on efficacy of CNN in diagnosing esophageal neoplasia, eight 5 14 16 18 19 22 23 25 reported on use of CNN in neoplasia of the stomach and six 2 11 13 20 21 24 evaluated use of CNN in diagnosing colorectal neoplasia.…”
Section: Resultsmentioning
confidence: 99%
“…Nineteen studies 2 5 6 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 reported on the detection and/ or classification of gastrointestinal neoplastic lesions by CNN. Among the 19 studies, five 6 12 15 17 26 reported on efficacy of CNN in diagnosing esophageal neoplasia, eight 5 14 16 18 19 22 23 25 reported on use of CNN in neoplasia of the stomach and six 2 11 13 20 21 24 evaluated use of CNN in diagnosing colorectal neoplasia. Seven studies 5 11 12 14 19 20 25 used standard WLE, eight used NBI (magnifying and/ or non-magnifying) 2 6 13 15 18 22 23 26 and four 16 17 21 24 used a combination of standard WLE and/or NBI and/or chromo-endoscopy images ( Table 1 ).…”
Section: Resultsmentioning
confidence: 99%
“…Twenty-seven studies were dedicated to improving diagnostic accuracy in case of colorectal polyps or cancer. Nineteen studies focused on the diagnosis of premalignant or malignant lesions of the upper gastrointestinal tract, [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57] only 4 studies were limited to the small bowel, [58][59][60][61] and 3 studies assessed the entire digestive tract. [62][63][64] Twenty-four studies used specific validation techniques-mainly k-fold cross- validation.…”
Section: Analysis Of Malignant and Premalignant Lesionsmentioning
confidence: 99%
“…Machine learning models are being intensively developed for the diagnosis phase to deduce and detect cancer from various available sources of medical images. In particular, the following examples were highly successful: A better prediction of breast cancer with a deep learning model combined with a logistic regression model (area under the curve (AUC) = 0.70 vs. 0.62, respectively) in which the image data and assessment records of over 39,571 subjects were combined [12]; the detection of early stage stomach cancer using a convolutional neural network (CNN, detection rate = 82.8%) [13]; the deduction of the risk level of lung cancer using a CNN model (AUC = 0.94) [14]. Regarding the treatment phase, machine learning is most used in cancer therapeutics.…”
Section: Machine Learning In Healthcarementioning
confidence: 99%