3D Convolutional

3D convolutional neural networks (CNNs) process three-dimensional data, extending 2D CNN capabilities to handle spatiotemporal information in video, volumetric data (e.g., medical scans, point clouds), and other 3D representations. Current research focuses on applying 3D CNNs in diverse fields, including video action recognition, medical image analysis (e.g., Alzheimer's disease diagnosis, stroke lesion segmentation), and 3D object reconstruction, often incorporating architectural enhancements like attention mechanisms and transformer integrations for improved performance and efficiency. This powerful technique is significantly impacting various scientific domains and practical applications by enabling more accurate and efficient analysis of complex, multi-dimensional data.