Author
Yao-Wen Xing
Bio: Yao-Wen Xing is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Feature extraction. The author has an hindex of 1, co-authored 1 publications receiving 42 citations.
Topics: Feature extraction
Papers
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TL;DR: A new computer-aided method to detect lesion images and provide worthwhile guidance for improving the efficiency and accuracy of gastrointestinal disease diagnosis and is a good prospect for clinical application.
53 citations
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TL;DR: The ways in which AI may help physicians make a diagnosis or establish a prognosis are reviewed and its limitations are discussed, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.
275 citations
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TL;DR: The results demonstrate that certain lncRNAs, such as AK001058, INHBA-as1, MIR4435-2HG, and CEBPA-AS1, are enriched in human gastric cancer tissues and significantly elevated in the plasma of patients with Gastric cancer.
Abstract: // Dong Ke 1, 2, 3 , Hanwei Li 2 , Yi Zhang 2, 3 , Yinghong An 4 , Hanjiang Fu 2 , Xuedong Fang 1, 3 , Xiaofei Zheng 2 1 General Surgery, The Second Hospital of Jilin University, Changchun, 130041, China 2 Beijing Key Laboratory for Radiobiology, Beijing institute of Radiation Medicine, Beijing, 100850, China 3 Gastrointestinal Colorectal and Anal Surgery, The China-Japan Union Hospital of Jilin University, Changchun, 130033, China 4 Clinical Laboratory Center, Chinese PLA Air Force General Hospital, Beijing, 100142, China Correspondence to: Hanjiang Fu, email: Fuhj75@126.com Xuedong Fang, email: fangxuedong@medmail.com.cn Xiaofei Zheng, email: xfzheng100@126.com Keywords: gastric cancer, long noncoding RNA, plasma, biomarker, RNA fragments Received: November 30, 2016 Accepted: February 14, 2017 Published: February 22, 2017 ABSTRACT Background: Suitable diagnostic markers for cancers are urgently required in clinical practice. Long non-coding RNAs, which have been reported in many cancer types, are a potential new class of biomarkers for tumor diagnosis. Results: Five lncRNAs, including AK001058, INHBA-AS1, MIR4435-2HG, UCA1 and CEBPA-AS1 were validated to be increased in gastric cancer tissues. Furthermore, we found that plasma level of these five lncRNAs were significantly higher in gastric cancer patients compared with normal controls. By receiver operating characteristic analysis, we found that the combination of plasma lncRNAs with the area under the curve up to 0.921, including AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1, is a better indicator of gastric cancer than their individual levels or other lncRNA combinations. Simultaneously, we found that the expression levels of a series of MIR4435-2HG fragments are different in gastric cancer plasma samples, but most of them higher than that in healthy control plasma samples. Materials and Methods: LncRNA gene expression profiles were analyzed in two pairs of human gastric cancer and adjacent non-tumor tissues by microarray analysis. Nine gastric cancer-associated lncRNAs were selected and assessed by quantitative real-time polymerase chain reaction in gastric tissues, and 5 of them were further analyzed in gastric cancer patients’ plasma. Conclusions: Our results demonstrate that certain lncRNAs, such as AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1, are enriched in human gastric cancer tissues and significantly elevated in the plasma of patients with gastric cancer. These findings indicate that the combination of these four lncRNAs might be used as diagnostic or prognostic markers for gastric cancer patients.
71 citations
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TL;DR: This review summarized and compared the latest published literature related to the common clinical GI diseases and covers the key applications of DL in GI image detection, classification, segmentation, recognition, location, and other tasks.
Abstract: Gastrointestinal (GI) disease is one of the most common diseases and primarily examined by GI endoscopy. Recently, deep learning (DL), in particular convolutional neural networks (CNNs) have made achievements in GI endoscopy image analysis. This review focuses on the applications of DL methods in the analysis of GI images. We summarized and compared the latest published literature related to the common clinical GI diseases and covers the key applications of DL in GI image detection, classification, segmentation, recognition, location, and other tasks. At the end, we give a discussion on the challenges and the research directions of GI image analysis based on DL in the future.
65 citations
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TL;DR: The current status of AI-applications was systematically summarized in gastric cancer and future directions that target this field were analyzed to overcome the risk of overfitting AI models and enhance their accuracy as well as the applicability in clinical practice.
Abstract: Gastric cancer is the fourth leading cause of cancer-related mortality across the globe, with a 5-year survival rate of less than 40%. In recent years, several applications of artificial intelligence (AI) have emerged in the gastric cancer field based on its efficient computational power and learning capacities, such as image-based diagnosis and prognosis prediction. AI-assisted diagnosis includes pathology, endoscopy, and computerized tomography, while researchers in the prognosis circle focus on recurrence, metastasis, and survival prediction. In this review, a comprehensive literature search was performed on articles published up to April 2020 from the databases of PubMed, Embase, Web of Science, and the Cochrane Library. Thereby the current status of AI-applications was systematically summarized in gastric cancer. Moreover, future directions that target this field were also analyzed to overcome the risk of overfitting AI models and enhance their accuracy as well as the applicability in clinical practice.
59 citations
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TL;DR: A comparative assessment of the models giving a new dimension to FNAC study where GoogLeNet-V3 (fine-tuned) achieved an accuracy of 96.25% which is highly satisfactory.
Abstract: Fine needle aspiration cytology (FNAC) entails using a narrow gauge (25-22 G) needle to collect a sample of a lesion for microscopic examination. It allows a minimally invasive, rapid diagnosis of tissue but does not preserve its histological architecture. FNAC is commonly used for diagnosis of breast cancer, with traditional practice being based on the subjective visual assessment of the breast cytopathology cell samples under a microscope to evaluate the state of various cytological features. Therefore, there are many challenges in maintaining consistency and reproducibility of findings. However, the advent of digital imaging and computational aid in diagnosis can improve the diagnostic accuracy and reduce the effective workload of pathologists. This paper presents a comparison of various deep convolutional neural network (CNN) based fine-tuned transfer learned classification approach for the diagnosis of the cell samples. The proposed approach has been tested using VGG16, VGG19, ResNet-50 and GoogLeNet-V3 (aka Inception V3) architectures of CNN on an image dataset of 212 images (99 benign and 113 malignant), later augmented and cleansed to 2120 images (990 benign and 1130 malignant), where the network was trained using images of 80% cell samples and tested on the rest. This paper presents a comparative assessment of the models giving a new dimension to FNAC study where GoogLeNet-V3 (fine-tuned) achieved an accuracy of 96.25% which is highly satisfactory.
52 citations