Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features
出版年份 2019 全文链接
标题
Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features
作者
关键词
-
出版物
EUROPEAN RADIOLOGY
Volume -, Issue -, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2019-05-16
DOI
10.1007/s00330-019-06214-8
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI
- (2019) Charlie A. Hamm et al. EUROPEAN RADIOLOGY
- Nonstandardized Terminology to Describe Focal Liver Lesions in Patients at Risk for Hepatocellular Carcinoma: Implications Regarding Clinical Communication
- (2018) Michael T. Corwin et al. AMERICAN JOURNAL OF ROENTGENOLOGY
- Interreader Reliability of LI-RADS Version 2014 Algorithm and Imaging Features for Diagnosis of Hepatocellular Carcinoma: A Large International Multireader Study
- (2018) Kathryn J. Fowler et al. RADIOLOGY
- Evidence Supporting LI-RADS Major Features for CT- and MR Imaging–based Diagnosis of Hepatocellular Carcinoma: A Systematic Review
- (2018) An Tang et al. RADIOLOGY
- LI-RADS: a glimpse into the future
- (2018) Claude B. Sirlin et al. Abdominal Radiology
- Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
- (2018) Pranav Rajpurkar et al. PLOS MEDICINE
- Diagnostic accuracy of prospective application of the Liver Imaging Reporting and Data System (LI-RADS) in gadoxetate-enhanced MRI
- (2017) Yeun-Yoon Kim et al. EUROPEAN RADIOLOGY
- Deep Learning: A Primer for Radiologists
- (2017) Gabriel Chartrand et al. RADIOGRAPHICS
- Management implications and outcomes of LI-RADS-2, -3, -4, and -M category observations
- (2017) Donald G. Mitchell et al. Abdominal Radiology
- Hepatocarcinogenesis and LI-RADS
- (2017) Kazim H. Narsinh et al. Abdominal Radiology
- Reliability, Validity, and Reader Acceptance of LI-RADS—An In-depth Analysis
- (2016) Borna K. Barth et al. ACADEMIC RADIOLOGY
- Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique
- (2016) Hayit Greenspan et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network
- (2016) Marios Anthimopoulos et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Liver Imaging Reporting and Data System: Review of Ancillary Imaging Features
- (2016) Irene Cruite et al. SEMINARS IN ROENTGENOLOGY
- Rate of observation and inter-observer agreement for LI-RADS major features at CT and MRI in 184 pathology proven hepatocellular carcinomas
- (2016) Eric C. Ehman et al. Abdominal Radiology
- Classifying CT/MR findings in patients with suspicion of hepatocellular carcinoma: Comparison of liver imaging reporting and data system and criteria-free Likert scale reporting models
- (2015) Yu-Dong Zhang et al. JOURNAL OF MAGNETIC RESONANCE IMAGING
- LI-RADS (Liver Imaging Reporting and Data System): Summary, discussion, and consensus of the LI-RADS Management Working Group and future directions
- (2014) Donald G. Mitchell et al. HEPATOLOGY
- Concordance of hypervascular liver nodule characterization between the organ procurement and transplant network and liver imaging reporting and data system classifications
- (2014) Mustafa R. Bashir et al. JOURNAL OF MAGNETIC RESONANCE IMAGING
- Repeatability of Diagnostic Features and Scoring Systems for Hepatocellular Carcinoma by Using MR Imaging
- (2014) Matthew S. Davenport et al. RADIOLOGY
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now