An interpretable deep neural network for colorectal polyp diagnosis under colonoscopy
出版年份 2021 全文链接
标题
An interpretable deep neural network for colorectal polyp diagnosis under colonoscopy
作者
关键词
Colorectal polyp diagnosis, Deep neural network, Yamada classification guidance, Polyp segmentation
出版物
KNOWLEDGE-BASED SYSTEMS
Volume 234, Issue -, Pages 107568
出版商
Elsevier BV
发表日期
2021-10-06
DOI
10.1016/j.knosys.2021.107568
参考文献
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注意：仅列出部分参考文献，下载原文获取全部文献信息。- Multiscale fused network with additive channel–spatial attention for image segmentation
- (2021) Chengling Gao et al. KNOWLEDGE-BASED SYSTEMS
- An interpretable knowledge-based decision support system and its applications in pregnancy diagnosis
- (2021) Kehui Song et al. KNOWLEDGE-BASED SYSTEMS
- Medical image segmentation based on active fusion-transduction of multi-stream features
- (2021) Yucheng Shu et al. KNOWLEDGE-BASED SYSTEMS
- Deep neural network correlation learning mechanism for CT brain tumor detection
- (2021) Marcin Woźniak et al. NEURAL COMPUTING & APPLICATIONS
- Multi-modal advanced deep learning architectures for breast cancer survival prediction
- (2021) Nikhilanand Arya et al. KNOWLEDGE-BASED SYSTEMS
- Recurrent generative adversarial networks for unsupervised WCE video summarization
- (2021) Libin Lan et al. KNOWLEDGE-BASED SYSTEMS
- ACCURACY OF ARTIFICIAL INTELLIGENCE ON HISTOLOGY PREDICTION AND DETECTION OF COLORECTAL POLYPS: A SYSTEMATIC REVIEW AND META-ANALYSIS
- (2020) Thomas KL. Lui et al. GASTROINTESTINAL ENDOSCOPY
- Deep Neural Networks approaches for detecting and classifying colorectal polyps
- (2020) Alba Nogueira-Rodríguez et al. NEUROCOMPUTING
- Deep learning to find colorectal polyps in colonoscopy: A systematic literature review
- (2020) Luisa F. Sánchez-Peralta et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- A descriptive framework for the field of deep learning applications in medical images
- (2020) Yingjie Tian et al. KNOWLEDGE-BASED SYSTEMS
- Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps
- (2019) Sudhir Sornapudi et al. Applied Sciences-Basel
- Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
- (2019) Ramprasaath R. Selvaraju et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Computer-aided diagnosis of gallbladder polyps based on high resolution ultrasonography
- (2019) Tao Chen et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Small Lung Nodules Detection Based on Fuzzy-Logic and Probabilistic Neural Network With Bioinspired Reinforcement Learning
- (2019) Giacomo Capizzi et al. IEEE TRANSACTIONS ON FUZZY SYSTEMS
- Cancer statistics, 2018
- (2018) Rebecca L. Siegel et al. CA-A CANCER JOURNAL FOR CLINICIANS
- Deep Learning Localizes and Identifies Polyps in Real Time with 96% Accuracy in Screening Colonoscopy
- (2018) Gregor Urban et al. GASTROENTEROLOGY
- Dermoscopic assisted diagnosis in melanoma: Reviewing results, optimizing methodologies and quantifying empirical guidelines
- (2018) Huei Diana Lee et al. KNOWLEDGE-BASED SYSTEMS
- Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker
- (2018) Ruikai Zhang et al. PATTERN RECOGNITION
- Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
- (2018) Freddie Bray et al. CA-A CANCER JOURNAL FOR CLINICIANS
- Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy
- (2018) Pu Wang et al. Nature Biomedical Engineering
- Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
- (2018) Scott M. Lundberg et al. Nature Biomedical Engineering
- Prevalence of Germline Mutations in Polyposis and Colorectal Cancer-associated Genes in Patients With Multiple Colorectal Polyps
- (2018) Peter P. Stanich et al. Clinical Gastroenterology and Hepatology
- Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
- (2017) Jeff Donahue et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
- (2017) David Vázquez et al. Journal of Healthcare Engineering
- Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos
- (2017) Lequan Yu et al. IEEE Journal of Biomedical and Health Informatics
- Edge density based automatic detection of inflammation in colonoscopy videos
- (2016) I. Ševo et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Clinicopathological Features of Colorectal Cancer Patients Under the Age of 50: Recent Experience and Case-Control Study of Prognosis in a Japanese Cohort
- (2016) Yuzo Nagai et al. DIGESTION
- Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
- (2016) Nima Tajbakhsh et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians
- (2015) Jorge Bernal et al. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
- Polyp Detection via Imbalanced Learning and Discriminative Feature Learning
- (2015) Seung-Hwan Bae et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- ImageNet Large Scale Visual Recognition Challenge
- (2015) Olga Russakovsky et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer
- (2013) Juan Silva et al. International Journal of Computer Assisted Radiology and Surgery
- Factors influencing the miss rate of polyps in a back-to-back colonoscopy study
- (2012) A. Leufkens et al. ENDOSCOPY
- Large Colorectal Polyps
- (2010) Sushil K. Ahlawat et al. JOURNAL OF CLINICAL GASTROENTEROLOGY
- Pedestrian Detection via Classification on Riemannian Manifolds
- (2008) O. Tuzel et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Factors that Increase Risk of Colon Polyps
- (2008) Sarah Grahn et al. Clinics in Colon and Rectal Surgery
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