Empirically, we verify the computational efficiency of our methods.

In this paper, we address this gap by dividing a typical patient's clinical journey

This gap hinders accurate sensory grounding in real-world scenarios.

In the present study, we introduce an innovative structure for 3D medical image segmentation that effectively integrates 2D U-Net-derived skip connections into the architecture of 3D convolutional neural networks (3D CNNs).

Ontologies are useful for automatic machine processing of domain knowledge as they represent it in a structured format.