3D Capsule

3D capsule networks are a type of neural network architecture designed to improve upon the limitations of convolutional neural networks (CNNs) by explicitly modeling hierarchical part-whole relationships within data. Current research focuses on enhancing efficiency through non-iterative routing algorithms and incorporating 3D capsule blocks into existing architectures like U-Nets and Vision Transformers, particularly for applications in medical image segmentation and other complex visual tasks. These advancements aim to improve the robustness, interpretability, and computational efficiency of deep learning models, leading to better performance in various fields including medical imaging, robotics, and natural language processing.