Journal of Digital Imaging

Papers
(The median citation count of Journal of Digital Imaging is 7. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2022-06-01 to 2026-06-01.)
ArticleCitations
Subject-Specific Automatic Reconstruction of White Matter Tracts77
ExpHBA Deep-IoT: Exponential Honey Badger Optimized Deep Learning For Breast Cancer Detection in IoT Healthcare System40
Automatic Liver Segmentation Using EfficientNet and Attention-Based Residual U-Net in CT27
CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations26
Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology24
Analyzing Transfer Learning of Vision Transformers for Interpreting Chest Radiography23
Application of Artificial Intelligence Computer-Assisted Diagnosis Originally Developed for Thyroid Nodules to Breast Lesions on Ultrasound23
Interactive Multimedia Reporting Technical Considerations: HIMSS-SIIM Collaborative White Paper23
IHE-Based Image Exchange in the Netherlands23
Neural Network Detection of Pacemakers for MRI Safety21
Classification of Mammographic ROI for Microcalcification Detection Using Multifractal Approach20
Correction to: Journal of Digital Imaging20
Medical Image Sharing in Japan18
Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis18
New Software for DQE Calculation in Digital Mammography Compliant with IEC 62220–1-218
A Deep Learning Image Reconstruction Algorithm for Improving Image Quality and Hepatic Lesion Detectability in Abdominal Dual-Energy Computed Tomography: Preliminary Results18
Image Exchange: an International Perspective17
Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review17
How Image Exchange Breaks Down: the Image Library Perspective14
FWLICM-Deep Learning: Fuzzy Weighted Local Information C-Means Clustering-Based Lung Lobe Segmentation with Deep Learning for COVID-19 Detection13
Initial Experience of 10 Imaging Vendors with the IHE SHARAZONE: a New Multivendor Peer-to-Peer Test Service for DICOM Objects13
An Explainable MRI-Radiomic Quantum Neural Network to Differentiate Between Large Brain Metastases and High-Grade Glioma Using Quantum Annealing for Feature Selection12
Pulmonary Surface Irregularity Score as a New Quantitative CT Marker for Idiopathic Pulmonary Fibrosis—a Pilot Study12
Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis11
Deep Transfer Learning-Based Approach for Glucose Transporter-1 (GLUT1) Expression Assessment10
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach10
A New Collaborative Classification Process for Microcalcification Detection Based on Graphs and Knowledge Propagation10
Unmasking Myocardial Dysfunction in Patients Hospitalized for Community-Acquired Pneumonia Using a 4-Chamber 3-Dimensional Volume/Strain Analysis9
Attachment Type, Thickness, and Volume of the Posterior Meniscofemoral Ligament and Meniscal Pathology9
Automated Cervical Spinal Cord Segmentation in Real-World MRI of Multiple Sclerosis Patients by Optimized Hybrid Residual Attention-Aware Convolutional Neural Networks9
Image Exchange in the Middle East: a Survey8
Recognition and Segmentation of Individual Bone Fragments with a Deep Learning Approach in CT Scans of Complex Intertrochanteric Fractures: A Retrospective Study7
Organs in Color: Utilizing Free Software and Emerging Multi Jet Fusion Technology to Color and Surface Label 3D-Printed Anatomical Models7
Comparison of Transfer Learning Models in Pelvic Tilt and Rotation Measurement in Pediatric Anteroposterior Pelvic Radiographs7
Automatic Brain Tumor Classification via Lion Plus Dragonfly Algorithm7
Bayesian Convolutional Neural Networks in Medical Imaging Classification: A Promising Solution for Deep Learning Limits in Data Scarcity Scenarios7
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