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Liver Fibrosis: Deep Convolutional Neural Network for Staging by Using Gadoxetic Acid–enhanced Hepatobiliary Phase MR Images
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Noninvasive Measures of Liver Fibrosis
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Staging Hepatic Fibrosis: Computer–Aided Analysis of Hepatic Contours on Gadolinium Ethoxybenzyl Diethylenetriaminepentaacetic Acid–Enhanced Hepatocyte–Phase Magnetic Resonance Imaging
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An Algorithm for the Grading of Activity in Chronic Hepatitis C
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Texture‐based classification of liver fibrosis using MRI
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Application of texture analysis on parametricT1andT2maps for detection of hepatic fibrosis
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Texture analysis of the liver at MDCT for assessing hepatic fibrosis
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Pathology for the HPB Surgeon
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CT Texture Analysis of Renal Masses
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Ultrasound-Based Transient Elastography for the Detection of Hepatic Fibrosis: Systematic Review and Meta-analysis
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Effective staging of fibrosis by the selected texture features of liver: Which one is better, CT or MR imaging?
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Quantitative computed tomography texture analysis for estimating histological subtypes of thymic epithelial tumors
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New Inuyama classification; new criteria for histological assessment of chronic hepatitis
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inferredcites →https://doi.org/10.1053/j.gastro.2008.01.034openalex-snapshot
Performance of Transient Elastography for the Staging of Liver Fibrosis: A Meta-Analysis
inferredcites →https://doi.org/10.1053/jhep.2000.16602openalex-snapshot
Practices of Liver Biopsy in France: Results of a Prospective Nationwide Survey
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Liver Fibrosis: Histopathologic and Biochemical Influences on Diagnostic Efficacy of Hepatobiliary Contrast-enhanced MR Imaging in Staging
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Locally Advanced Squamous Cell Carcinoma of the Head and Neck: CT Texture and Histogram Analysis Allow Independent Prediction of Overall Survival in Patients Treated with Induction Chemotherapy
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Liver Surface Nodularity as a Biomarker for Detection and Evaluation of Cirrhosis
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Liver Fibrosis: Noninvasive Assessment with MR Elastography versus Aspartate Aminotransferase–to-Platelet Ratio Index
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Hepatitis C Related Chronic Liver Cirrhosis: Feasibility of Texture Analysis of MR Images for Classification of Fibrosis Stage and Necroinflammatory Activity Grade
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Computer-Aided Diagnosis of Hepatic Fibrosis: Preliminary Evaluation of MRI Texture Analysis Using the Finite Difference Method and an Artificial Neural Network
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Accuracy of Liver Surface Nodularity Quantification on MDCT as a Noninvasive Biomarker for Staging Hepatic Fibrosis
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Deep Learning Methods in the Imaging of Hepatic and Pancreaticobiliary Diseases
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Possibility of Deep Learning in Medical Imaging Focusing Improvement of Computed Tomography Image Quality
inferred← citeshttps://doi.org/10.1097/rct.0000000000001169openalex-snapshot
Current Status of Radiomics and Deep Learning in Liver Imaging
inferred← citeshttps://doi.org/10.1101/2022.03.25.22272965openalex-snapshot
Machine Learning Enabled Non-invasive Diagnosis of Nonalcoholic Fatty Liver Disease and Assessment of Abdominal Fat from MRI Data
inferred← citeshttps://doi.org/10.1109/bibe50027.2020.00166openalex-snapshot
Deep Learning based NAS Score and Fibrosis Stage Prediction from CT and Pathology Data
inferred← citeshttps://doi.org/10.1109/cises54857.2022.9844330openalex-snapshot
Detection of COVID-19 using CNN from Chest X-ray Images
inferred← citeshttps://doi.org/10.1109/cisp-bmei48845.2019.8965874openalex-snapshot
HCC Recognition Within Ultrasound Images Employing Advanced Textural Features with Deep Learning Techniques
inferred← citeshttps://doi.org/10.1109/cscloud62866.2024.00023openalex-snapshot
Liver Fibrosis Classification based on Multimodal Imaging Feature Fusion
inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.01072openalex-snapshot
XFibrosis: Explicit Vessel-Fiber Modeling for Fibrosis Staging from Liver Pathology Images
inferred← citeshttps://doi.org/10.1109/icbcb55259.2022.9802126openalex-snapshot
Combining the Transformers and CNNs for Renal Parenchymal Tumors Diagnosis
inferred← citeshttps://doi.org/10.1109/icmipe53131.2021.9698912openalex-snapshot
Ultrasonic image fibrosis staging based on machine learning for chronic liver disease
inferred← citeshttps://doi.org/10.1109/isbi48211.2021.9434170openalex-snapshot
Multi-Modal Active Learning For Automatic Liver Fibrosis Diagnosis Based On Ultrasound Shear Wave Elastography
inferred← citeshttps://doi.org/10.1109/tmi.2022.3175461openalex-snapshot
Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM
inferred← citeshttps://doi.org/10.1111/jgh.15414openalex-snapshot
Radiomics and deep learning in liver diseases
inferred← citeshttps://doi.org/10.1111/liv.14966openalex-snapshot
Deep learning for noninvasive liver fibrosis classification: A systematic review
inferred← citeshttps://doi.org/10.1117/12.2590861openalex-snapshot
Online system for staging hepatic fibrosis
inferred← citeshttps://doi.org/10.1148/radiol.2018180547openalex-snapshot
Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide
inferred← citeshttps://doi.org/10.1148/radiol.2018180763openalex-snapshot
Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent–enhanced CT Images in the Liver
inferred← citeshttps://doi.org/10.1148/radiol.2018181197openalex-snapshot
Radiomics Analysis of Gadoxetic Acid–enhanced MRI for Staging Liver Fibrosis
inferred← citeshttps://doi.org/10.1148/rg.2021210105openalex-snapshot
Deep Learning–based Reconstruction for Lower-Dose Pediatric CT: Technical Principles, Image Characteristics, and Clinical Implementations
inferred← citeshttps://doi.org/10.1148/rg.220133openalex-snapshot
Clinical Impact of Deep Learning Reconstruction in MRI
inferred← citeshttps://doi.org/10.1148/ryai.2021200274openalex-snapshot
Deep Learning Prediction of Voxel-Level Liver Stiffness in Patients with Nonalcoholic Fatty Liver Disease
inferred← citeshttps://doi.org/10.1155/2018/1753480openalex-snapshot
Classification of Computed Tomography Images in Different Slice Positions Using Deep Learning
inferred← citeshttps://doi.org/10.1155/2019/9783106openalex-snapshot
Noninvasive Evaluation of the Pathologic Grade of Hepatocellular Carcinoma Using MCF-3DCNN: A Pilot Study
inferred← citeshttps://doi.org/10.1155/2022/2249447openalex-snapshot
Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis
inferred← citeshttps://doi.org/10.1159/000503291openalex-snapshot
Preoperative Prediction of Pancreatic Neuroendocrine Neoplasms Grading Based on Enhanced Computed Tomography Imaging: Validation of Deep Learning with a Convolutional Neural Network
inferred← citeshttps://doi.org/10.1177/02841851251349495openalex-snapshot
Fine-tuned large language model for classifying CT-guided interventional radiology reports
inferred← citeshttps://doi.org/10.1186/s12876-020-01585-5openalex-snapshot
Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis
inferred← citeshttps://doi.org/10.1186/s12876-023-03067-wopenalex-snapshot
A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination
inferred← citeshttps://doi.org/10.1186/s12880-024-01272-xopenalex-snapshot
Role of radiomics in staging liver fibrosis: a meta-analysis
inferred← citeshttps://doi.org/10.1186/s12967-024-05726-2openalex-snapshot
Artificial intelligence-based evaluation of prognosis in cirrhosis
inferred← citeshttps://doi.org/10.1186/s13244-019-0738-2openalex-snapshot
What the radiologist should know about artificial intelligence – an ESR white paper
inferred← citeshttps://doi.org/10.1186/s13244-024-01872-9openalex-snapshot
Fully automated MRI-based convolutional neural network for noninvasive diagnosis of cirrhosis
inferred← citeshttps://doi.org/10.1186/s13640-020-00520-8openalex-snapshot
A study of hepatic fibrosis staging methods using diffraction enhanced imaging
inferred← citeshttps://doi.org/10.1186/s41747-020-00148-3openalex-snapshot
Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach
inferred← citeshttps://doi.org/10.1259/bjr.20211136openalex-snapshot
Clinical application of deep learning and radiomics in hepatic disease imaging: a systematic scoping review
inferred← citeshttps://doi.org/10.1259/bjr.20220685openalex-snapshot
Effects of deep learning on radiologists’ and radiology residents’ performance in identifying esophageal cancer on CT
inferred← citeshttps://doi.org/10.1371/journal.pmed.1002707openalex-snapshot
Deep learning and artificial intelligence in radiology: Current applications and future directions
inferred← citeshttps://doi.org/10.14218/jcth.2021.00447openalex-snapshot
Noninvasive Assessment of Liver Fibrosis and Inflammation in Chronic Hepatitis B: A Dual-task Convolutional Neural Network (DtCNN) Model Based on Ultrasound Shear Wave Elastography
inferred← citeshttps://doi.org/10.14309/ctg.0000000000000616openalex-snapshot
Using Artificial Intelligence to Predict Cirrhosis From Computed Tomography Scans
inferred← citeshttps://doi.org/10.20892/j.issn.2095-3941.2018.0018openalex-snapshot
Progress in non-invasive detection of liver fibrosis
inferred← citeshttps://doi.org/10.21037/qims-23-20openalex-snapshot
An artificial intelligence-assisted diagnosis modeling software (AIMS) platform based on medical images and machine learning: a development and validation study
inferred← citeshttps://doi.org/10.21203/rs.3.rs-1444366/v1openalex-snapshot
CT-Based Deep Learning Enables Postoperative Recurrence Prediction for Intrahepatic Cholangiocarcinoma
inferred← citeshttps://doi.org/10.21203/rs.3.rs-2475668/v1openalex-snapshot
A Fully Automated Hybrid Approach to Assessing Liver Fibrosis and Necroinflammation on Conventional MRI: A Multi-Center Cohort Study
inferred← citeshttps://doi.org/10.2174/9789815136807123010009openalex-snapshot
Artificial Intelligence (AI) Game Changer in Cancer Biology
inferred← citeshttps://doi.org/10.2196/preprints.35949openalex-snapshot
Artificial Intelligence Technologies for Detection and Quantification of Hepatic Steatosis: A Scoping Review (Preprint)
inferred← citeshttps://doi.org/10.2214/ajr.19.22145openalex-snapshot
Interpretable Artificial Intelligence: Why and When
inferred← citeshttps://doi.org/10.23736/s2723-9284.23.00252-0openalex-snapshot
Machine learning and big data in precision medicine: what is the role of the Radiologist?
inferred← citeshttps://doi.org/10.23838/pfm.2018.00030openalex-snapshot
Prospects of deep learning for medical imaging
inferred← citeshttps://doi.org/10.2463/mrms.rev.2022-0102openalex-snapshot
Update on the Use of Artificial Intelligence in Hepatobiliary MR Imaging
inferred← citeshttps://doi.org/10.2957/kanzo.61.637openalex-snapshot
Deep learning and diagnostic radiology: application to the liver CT and MRI
inferred← citeshttps://doi.org/10.3346/jkms.2018.33.e152openalex-snapshot
Connecting Technological Innovation in Artificial Intelligence to Real-world Medical Practice through Rigorous Clinical Validation: What Peer-reviewed Medical Journals Could Do
inferred← citeshttps://doi.org/10.3348/jksr.2018.78.5.301openalex-snapshot
Artificial Intelligence in Medicine: Beginner's Guide
inferred← citeshttps://doi.org/10.3348/kjr.2019.0752openalex-snapshot
Radiomics and Deep Learning: Hepatic Applications
inferred← citeshttps://doi.org/10.3389/fbioe.2022.872044openalex-snapshot
Pretreatment Computed Tomography-Based Machine Learning Models to Predict Outcomes in Hepatocellular Carcinoma Patients who Received Combined Treatment of Trans-Arterial Chemoembolization and Tyrosine Kinase Inhibitor
inferred← citeshttps://doi.org/10.3389/fmed.2022.839919openalex-snapshot
Assessment of Liver Function With MRI: Where Do We Stand?
inferred← citeshttps://doi.org/10.3389/fmed.2025.1591523openalex-snapshot
Artificial intelligence in imaging for liver disease diagnosis
inferred← citeshttps://doi.org/10.3389/fmed.2025.1678753openalex-snapshot
Liver biopsy in the modern era: from traditional techniques to artificial intelligence and multi-omics integration
inferred← citeshttps://doi.org/10.3389/fnins.2018.00804openalex-snapshot
Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning
inferred← citeshttps://doi.org/10.3389/fonc.2021.588010openalex-snapshot
Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Networks
inferred← citeshttps://doi.org/10.3389/fonc.2022.1033478openalex-snapshot
Development of a hepatocellular carcinoma imaging database and structured imaging reports based on PACS, HIS, and repository
inferred← citeshttps://doi.org/10.3389/fonc.2022.927868openalex-snapshot
Automatic detection of early gastric cancer in endoscopy based on Mask region-based convolutional neural networks (Mask R-CNN)(with video)
inferred← citeshttps://doi.org/10.3390/app10124245openalex-snapshot
Development of a Deep Learning-Based Algorithm to Detect the Distal End of a Surgical Instrument
inferred← citeshttps://doi.org/10.3390/app9040682openalex-snapshot
Evaluating the Overall Accuracy of Additional Learning and Automatic Classification System for CT Images
inferred← citeshttps://doi.org/10.3390/biom11020307openalex-snapshot
Imaging-Based Staging of Hepatic Fibrosis in Patients with Hepatitis B: A Dynamic Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI
inferred← citeshttps://doi.org/10.3390/diagnostics11081373openalex-snapshot
Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images
inferred← citeshttps://doi.org/10.3390/diagnostics12020550openalex-snapshot
Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging
inferred← citeshttps://doi.org/10.3390/diagnostics14232646openalex-snapshot
Hybrid Deep Learning and Machine Learning for Detecting Hepatocyte Ballooning in Liver Ultrasound Images
inferred← citeshttps://doi.org/10.3390/ijerph182010811openalex-snapshot
Deep Ensemble Learning Approaches in Healthcare to Enhance the Prediction and Diagnosing Performance: The Workflows, Deployments, and Surveys on the Statistical, Image-Based, and Sequential Datasets
inferred← citeshttps://doi.org/10.3390/jcm14176231openalex-snapshot
Diagnostic Applications of Artificial Intelligence in Liver Diseases
inferred← citeshttps://doi.org/10.3390/jcm14238466openalex-snapshot
Meta-Analysis of AI Integration in Abdominal Imaging for Liver Fibrosis and MASLD: Evaluating Diagnostic Accuracy and Clinical Impact
inferred← citeshttps://doi.org/10.3390/s20113085openalex-snapshot
Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images
inferred← citeshttps://doi.org/10.34133/2021/8786793openalex-snapshot
Advances in Deep Learning-Based Medical Image Analysis
inferred← citeshttps://doi.org/10.35772/ghm.2023.01085openalex-snapshot
Gallbladder fossa nodularity in the liver as observed in alcoholic liver disease patients: Analysis based on hepatobiliary phase signal intensity on gadoxetate-enhanced MRI and extracellular volume fraction calculated from routine CT data
inferred← citeshttps://doi.org/10.3748/wjg.v25.i6.672openalex-snapshot
Artificial intelligence in medical imaging of the liver
inferred← citeshttps://doi.org/10.3748/wjg.v26.i25.3660openalex-snapshot
Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography
inferred← citeshttps://doi.org/10.3748/wjg.v27.i34.5715openalex-snapshot
Artificial intelligence for hepatitis evaluation
inferred← citeshttps://doi.org/10.3748/wjg.v27.i40.6794openalex-snapshot
Artificial intelligence in gastroenterology: A state-of-the-art review
inferred← citeshttps://doi.org/10.3748/wjg.v27.i40.6825openalex-snapshot
Emerging artificial intelligence applications in liver magnetic resonance imaging
inferred← citeshttps://doi.org/10.3748/wjg.v31.i46.111176openalex-snapshot
Artificial intelligence in hepatopathy diagnosis and treatment: Big data analytics, deep learning, and clinical prediction models
inferred← citeshttps://doi.org/10.4236/etsn.2021.102003openalex-snapshot
Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning
inferred← citeshttps://doi.org/10.5009/gnl18384openalex-snapshot
Overview of Deep Learning in Gastrointestinal Endoscopy
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