Li and colleagues developed a deep-learning model to analyze EEG recordings and detect event-level EEG spikes. 2. The model achieved high accuracy and a low false-positive rate, with only 32% of human ...
Emerging Technologies in Neuro-Oncology: AI, Intraoperative Imaging, Augmented Reality, and Robotics
Technological innovation is rapidly transforming neuro-oncologic surgery, yet a major unmet need remains: translating promising tools into reliable, ...
The proposed CNN-based system demonstrates the feasibility and robustness of deep learning for automatic lung nodule detection and classification. Despite strong results, the study acknowledges ...
Conavi is advancing the development and commercialization of its next-generation hybrid IVUS-OCT imaging solutions and has submitted its next-generation Novasight imaging system to the U.S. Food and ...
Tu, H. and Huang, Y.Y. (2026) Progress in Quantitative Imaging Assessment of Dermatomyositis-Associated Interstitial Lung ...
Abstract: Synthetic aperture radar (SAR) image target detection and recognition (SAR-TDR) tasks have become research hot spots in the remote sensing application. These targets include ships, vehicles, ...
Researchers have developed a new artificial intelligence-based approach for detecting fatty deposits inside coronary arteries using optical coherence tomography (OCT) images. Because these lipid-rich ...
Abstract: CT scanning yields useful information on the internal anatomy or pathology of the human body. Researchers are always working to use low radiation doses to create CT that is both high-quality ...
Ensemble integrating three architectures achieved area under the curve of 0.9208, outperforming individual models.
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