COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS(英文缩写 COMPUT MED IMAG GRAP),ISSN 0895-6111,eISSN 1879-0771 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-09-21 至 2026-09-21，按本站收录文献的发表日期统计。
指标来源：jcr_cas_ifqb
期刊简介
暂无简介。
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 7.422 | Q1 |
| 2022 | 5.700 | Q1 |
| 2023 | 5.400 | Q1 |
| 2024 | 4.900 | Q1 |
| 2025 | 5.500 | Q1 |
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 最新收录文献
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1. End-to-end framework integrating generative AI and deep reinforcement learning for autonomous ultrasound scanning.
PMID:作者:期刊:日期:2026-09-07Cardiac ultrasound (US) is among the most widely used diagnostic tools in cardiology for assessing heart health, but its effectiveness is limited by operator dependence, time constraints, and human error. The shortage of trained professionals, especially in remote areas, further restricts access. Th…
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2. DARTS-searched feedback-augmented state space model for 3D pulmonary nodule classification.
PMID:作者:期刊:日期:2026-09-03The automatic classification of pulmonary nodules is important for early cancer diagnosis. In this work, we aim to find the optimal state space model (SSM) for pulmonary nodule classification by leveraging Differentiable Architecture Search (DARTS). To achieve DARTS, we design the Differentiable Sta…
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3. TeF-SAM: Prototype memory for medical lesion segmentation with text-free inference.
PMID:作者:期刊:日期:2026-08-31Medical lesion segmentation often depends on large, high-quality pixel-level annotations, which are costly to obtain because they require expert delineation. Recent multimodal methods use clinical text to improve visual representation learning, but they usually require paired image-report data durin…
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4. 3D-FuseNet: An innovative multimodal fusion network for enhancing survival prediction.
PMID:作者:期刊:日期:2026-08-29Predicting the survival time of brain tumor patients remains a critical challenge in the medical prognosis. Existing methods often struggle to integrate heterogeneous data types, such as imaging and clinical records. This paper proposes a novel approach, the Three-Dimensional Feature Fusion Network …
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5. Semi-supervised medical image segmentation via Cross Reliable Knowledge Distillation.
PMID:作者:期刊:日期:2026-08-25Semi-supervised medical image segmentation aims to alleviate the dependence on large-scale annotations by exploiting unlabeled data. However, existing methods often lack effective mechanisms for leveraging semantic knowledge from labeled data and enabling reliable knowledge interaction among unlabel…
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6. Enhancing semi-supervised skin lesion segmentation with text-guided pseudo descriptions.
PMID:作者:期刊:日期:2026-08-20Accurate segmentation of skin lesions in dermoscopy images remains challenging due to the scarcity of densely annotated medical images, as manual pixel-level annotation demands significant expert effort. Conversely, textual descriptions are more readily obtainable and can potentially provide rich se…
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7. SOM-GAN: A structure-preserving one-to-multiple generative adversarial network for unpaired medical image synthesis.
PMID:作者:期刊:日期:2026-08-20GAN-based image translation has been widely used for cross-domain medical image synthesis. However, most existing methods follow a one-to-one mapping paradigm, requiring a separate model for each target domain and increasing the training cost in multi-target tasks such as multi-sequence MRI synthesi…
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8. OOD-aware Reliability Learning for open-world semi-supervised liver lesion recognition.
PMID:作者:期刊:日期:2026-08-17Prelocalized focal liver lesion classification from multi-phase magnetic resonance imaging remains challenging in multi-center clinical practice because lesion-level annotation is limited, imaging protocols vary across institutions, and routine unlabeled data may contain categories outside the prede…
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9. Global-Local Feature Fusion and SIREN-enhanced geometric supervision for coronary artery segmentation in CCTA.
PMID:作者:期刊:日期:2026-08-17Accurate segmentation of coronary arteries from Coronary Computed Tomography Angiography (CCTA) is essential for automated cardiovascular disease diagnosis and risk stratification. However, this task presents a fundamental global-local dilemma: modeling the topology and continuity of the extensive v…
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10. Deep learning for multimodal brain tumor segmentation: Architectures, fusion, robust learning, and deployment perspectives.
PMID:作者:期刊:日期:2026-08-01Accurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, radiotherapy targeting, and longitudinal assessment. Deep learning has advanced this task through convolutional neural networks, Transformers, state-space models, di…