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Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

Computerized Medical Imaging and Graphics is the official journal of the Computerized Medical Imaging Society. This journal focuses on the advancements and applications of computerized medical imaging techniques, including but not limited to, radiology, ultrasound, magnetic resonance imaging (MRI), and computed tomography (CT). It publishes research articles, reviews, and case studies that contribute to the understanding and development of computerized medical imaging and graphics in healthcare.
Details
Abbr. Comput Med Imaging Graph
Start 1988
End Continuing
Frequency Bimonthly
p-ISSN 0895-6111
e-ISSN 1879-0771
Country United States
Language English
Specialty Radiology
Metrics
h-index / Ranks: 3490 93
SJR / Ranks: 2054 1459
CiteScore / Ranks: 884 12.40
JIF / Ranks: 1231 5.7
Recent Articles
11.
Subtraction-free artifact-aware digital subtraction angiography image generation for head and neck vessels from motion data
Liu Y, Du D, Liu Y, Tu S, Yang W, Han X, et al.
Comput Med Imaging Graph . 2025 Feb; 121:102512. PMID: 39983664
Digital subtraction angiography (DSA) is an essential diagnostic tool for analyzing and diagnosing vascular diseases. However, DSA imaging techniques based on subtraction are prone to artifacts due to misalignments between...
12.
Prior knowledge-based multi-task learning network for pulmonary nodule classification
Xue P, Lu H, Fu Y, Ji H, Ren M, Xiao T, et al.
Comput Med Imaging Graph . 2025 Feb; 121:102511. PMID: 39970821
The morphological characteristics of pulmonary nodule, also known as the attributes, are crucial for classification of benign and malignant nodules. In clinical, radiologists usually conduct a comprehensive analysis of correlations...
13.
Automatic Joint Lesion Detection by enhancing local feature interaction
Liu Y, Wang T, Yang L, Wu J, He T
Comput Med Imaging Graph . 2025 Feb; 121:102509. PMID: 39947085
Recently, deep learning models have demonstrated impressive performance in Automatic Joint Lesion Detection (AJLD), yet balancing accuracy and efficiency remains a significant challenge. This paper focuses on achieving end-to-end lesion...
14.
Interpretable multi-stage attention network to predict cancer subtype, microsatellite instability, TP53 mutation and TMB of endometrial and colorectal cancer
Wang C, Muzakky H, Lee Y, Chung Y, Wang Y, Yu M, et al.
Comput Med Imaging Graph . 2025 Feb; 121:102499. PMID: 39947084
Mismatch repair deficiency (dMMR), also known as high-grade microsatellite instability (MSI-H), is a well-established biomarker for predicting the immunotherapy response in endometrial cancer (EC) and colorectal cancer (CRC). Tumor mutational...
15.
TQGDNet: Coronary artery calcium deposit detection on computed tomography
Wang W, Yu C, Ahn E, Pathan S, Negishi K, Kim J
Comput Med Imaging Graph . 2025 Feb; 121:102503. PMID: 39947083
Coronary artery disease (CAD) continues to be a leading global cause of cardiovascular related mortality. The scoring of coronary artery calcium (CAC) using computer tomography (CT) images is a diagnostic...
16.
Entity-level multiple instance learning for mesoscopic histopathology images classification with Bayesian collaborative learning and pathological prior transfer
He Q, Xu Y, Huang Q, Li J, He Y, Wang Z, et al.
Comput Med Imaging Graph . 2025 Feb; 121:102495. PMID: 39921929
Background: Entity-level pathologic structures with independent structures and functions are at a mesoscopic scale between the cell-level and slide-level, containing limited structures thus providing fewer instances for multiple instance learning....
17.
CGNet: Few-shot learning for Intracranial Hemorrhage Segmentation
Gong W, Luo Y, Yang F, Zhou H, Lin Z, Cai C, et al.
Comput Med Imaging Graph . 2025 Feb; 121:102505. PMID: 39921928
In recent years, with the increasing attention from researchers towards medical imaging, deep learning-based image segmentation techniques have become mainstream in the field, requiring large amounts of manually annotated data....
18.
DLPVI: Deep learning framework integrating projection, view-by-view backprojection, and image domains for high- and ultra-sparse-view CBCT reconstruction
Zhao X, Du Y, Peng Y
Comput Med Imaging Graph . 2025 Feb; 121:102508. PMID: 39921927
This study proposes a deep learning framework, DLPVI, which integrates projection, view-by-view backprojection (VVBP), and image domains to improve the quality of high-sparse-view and ultra-sparse-view cone-beam computed tomography (CBCT) images....
19.
Weakly supervised multi-modal contrastive learning framework for predicting the HER2 scores in breast cancer
Shi J, Sun D, Jiang Z, Du J, Wang W, Zheng Y, et al.
Comput Med Imaging Graph . 2025 Feb; 121:102502. PMID: 39919535
Human epidermal growth factor receptor 2 (HER2) is an important biomarker for prognosis and prediction of treatment response in breast cancer (BC). HER2 scoring is typically evaluated by pathologist microscopic...
20.
ProtoSAM-3D: Interactive semantic segmentation in volumetric medical imaging via a Segment Anything Model and mask-level prototypes
Shen Y, Dreizin D, Inigo B, Unberath M
Comput Med Imaging Graph . 2025 Feb; 121:102501. PMID: 39919534
Semantic segmentation of volumetric medical images is essential for accurate delineation of anatomic structures and pathology, enabling quantitative analysis in precision medicine applications. While volumetric segmentation has been extensively studied,...