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1. Deepfake Detection in Cancer Medical Imaging Using CNN Architectures NSTL国家科技图书文献中心

Dima Talal Alhalabi |  Moatsum Alawida... -  《Web Information Systems Engineering - WISE 2024 PhD Symposium,Demos and Workshops》 -  International Conference on Web Information Systems Engineering - 2025, - 299~312 - 共14页

摘要: in the medical field, as its potential to |  manipulate medical scans could result in misdiagnoses |  addresses the urgent need for robust detection methods to |  safeguard medical imaging systems and patient safety. We |  developed a deepfake detection model by evaluating four
关键词: Deepfake detection |  Medical images |  Convolutional neural networks (CNNs) |  Machine learning

2. Learning Frequency and Structure in UDA for Medical Object Detection NSTL国家科技图书文献中心

Liwen Wang |  Xiaoyan Zhang... -  《Pattern Recognition and Computer Vision,Part XIV》 -  Chinese Conference on Pattern Recognition and Computer Vision - 2025, - 518~532 - 共15页

摘要:In medical imaging applications |  structure detection is crucial for diagnosing cardiac |  fetal cardiac structure detection. The method |  in medical images. Specifically, we introduce a |  Structure Alignment (OSA) module to mitigate detection
关键词: Unsupervised domain adaption |  Medical image analysis |  Object detection

3. Industrial and medical anomaly detection through cycle-consistent adversarial networks NSTL国家科技图书文献中心

Bougaham A. |  Delchevalerie V.... -  《Neurocomputing》 - 2025,614(Jan.21) - 1.1~1.11 - 共11页

摘要: Anomaly Detection (AD) approach for industrial and |  medical images is proposed. This method leverages the |  method is evaluated on industrial and medical datasets | © 2024 Elsevier B.V.In this study, a new |  theoretical strengths of unsupervised learning and the data
关键词: Anomaly detection |  Cycle-GAN |  Industrial images |  Industry 4.0 |  Medical images |  Zero false negative

4. Lung Cancer Detection Systems Applied to Medical Images: A State-of-the-Art Survey NSTL国家科技图书文献中心

Sher Lyn Tan |  Ganeshsree Selvachan...... -  《Archives of computational methods in engineering》 - 2025,32(1) - 343~380 - 共38页

摘要: automated lung cancer detection within medical imaging |  of age, gender, and ethnicity. Timely detection |  targeting innovative models tailored solely for medical |  advancements in lung cancer detection systems. To ensure |  the study of automated lung cancer detection and
关键词: Computer-aided diagnosis (CAD) |  Lung cancer detection |  Pulmonary nodules detection |  Convolutional neural network (CNN) |  Medical image processing |  Deep learning

5. A pseudo-3D coarse-to-fine architecture for 3D medical landmark detection NSTL国家科技图书文献中心

Cui L. |  Guo J.... -  《Neurocomputing》 - 2025,614(Jan.21) - 1.1~1.10 - 共10页

摘要: medical landmark detection. However, incorporating 3D |  3D medical images. On the other hand, the SS-P3D | ) while simultaneously improving landmark detection |  mandibular molar landmark detection and a private dataset |  for cephalometric landmark detection. Overall, this
关键词: 3D medical landmark detection |  Cross-plane feature interaction |  Model parameter optimization

6. Radiation imaging detection and medical thermal modeling assisted diagnosis in cognitive intervention for Alzheimer's disease: A meta-analysis NSTL国家科技图书文献中心

Huang, Donghua |  Sun, Yingfang... -  《Thermal science and engineering progress》 - 2025,57 - 共8页

摘要: radiographic imaging detection and medical thermal modeling |  medical thermal modeling assisted diagnosis. This study | , study design, etc. The Medical thermal Modeling | . In contrast, medical thermal modeling techniques | Cognitive dysfunction is a typical clinical
关键词: Alzheimer's disease |  Cognitive intervention |  Radiographic imaging detection |  Medical thermal modeling |  Auxiliary diagnosis |  Meta-analysis

7. Robust Curve Detection in Volumetric Medical Imaging via Attraction Field NSTL国家科技图书文献中心

Farukh Yaushev |  Daria Nogina... -  《Shape in Medical Imaging》 -  International Workshop on Shape in Medical Imaging |  International Conference on Medical Image Computing and Computer Assisted Intervention - 2025, - 84~96 - 共13页

摘要: for precise medical diagnostics. Curves effectively |  medical imaging applications related to cardiovascular |  detection methods are often task-specific, relying heavily | -detection. | Understanding body part geometry is crucial
关键词: Curve detection |  Attraction fields |  Deep learning |  Subpixel-level accuracy

8. Thermal image edge detection for AI‑powered medical research imaging NSTL国家科技图书文献中心

Hamid Hoorfar |  Adam C. Puche... -  《The Journal of Supercomputing》 - 2025,81(4) - 629.1~629.33 - 共33页

摘要: in medical research and diagnostics, offering a |  facial temperatures. To enhance boundary detection | , edge detection methods were applied as a |  integrating these edge detection techniques, the accuracy of | Infrared thermal imaging has emerged as a tool
关键词: Convolutional neural network |  Thermal image processing |  Infrared image |  Edge detection |  Hot flush disease

9. MedIAnomaly: A comparative study of anomaly detection in medical images NSTL国家科技图书文献中心

Cai, Yu |  Zhang, Weiwen... -  《Medical image analysis》 - 2025,102 - Article 103500~Article 103500 - 共22页

摘要:Anomaly detection (AD) aims at detecting |  recognition and health screening in the medical domain | . Despite the emergence of numerous methods for medical AD | . Seven medical datasets with five image modalities |  abnormal samples that deviate from the expected normal
关键词: Anomaly detection |  Survey |  Benchmark

10. Class imbalance-aware domain specific transfer learning approach for medical image classification: Application on COVID-19 detection NSTL国家科技图书文献中心

Marut Jindal |  Birmohan Singh -  《Engineering Applications of Artificial Intelligence》 - 2025,150(Jun.) - 110583.1~110583.20 - 共20页

摘要: promise in medical image classification; however, their |  medical imaging tasks. CIDSTL-Net adopts a two-stage |  models followed by fine-tuning on targeted medical | , CIDSTL-Net holds significant potential to aid medical | Convolutional Neural Networks (CNNs) show
关键词: Transfer learning |  Medical image classification |  COVID detection |  Domain-aware learning |  Data imbalance management
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