Volumetric Medical

Volumetric medical image analysis focuses on extracting meaningful information from three-dimensional medical scans, aiming to improve diagnostic accuracy and treatment planning. Current research emphasizes developing robust and efficient algorithms for tasks like segmentation (identifying specific organs or lesions), super-resolution (enhancing image detail), and compression (reducing storage needs), often employing deep learning architectures such as convolutional neural networks and transformers, along with implicit neural representations. These advancements are crucial for improving the speed and accuracy of medical image analysis, leading to better patient care and facilitating large-scale studies. Furthermore, research is actively addressing challenges like handling anisotropic data, improving the explainability of AI models, and developing methods for efficient few-shot learning and out-of-distribution detection.