Intracranial Hemorrhage

Intracranial hemorrhage (ICH), bleeding within the skull or brain, is a life-threatening condition demanding rapid and accurate diagnosis for effective treatment. Current research focuses on developing AI-powered tools, employing deep learning architectures like U-Nets, Vision Transformers, and convolutional neural networks often combined with graph neural networks, to improve ICH detection, segmentation, and prognosis prediction from CT scans. These models leverage both image and clinical data, aiming to surpass human performance in speed and accuracy, ultimately improving patient outcomes and streamlining clinical workflows. The development of large, well-annotated datasets and novel weakly supervised methods are also key areas of focus to address data scarcity and annotation challenges.

Papers