Intracerebral Hemorrhage

Intracerebral hemorrhage (ICH), the bleeding within the brain, is a life-threatening condition demanding rapid diagnosis and effective treatment strategies. Current research heavily utilizes deep learning, employing architectures like U-Nets, Vision Transformers, and recurrent neural networks, to analyze CT scans for accurate hemorrhage detection, segmentation, and volume estimation, often incorporating attention mechanisms to improve performance. These advancements aim to improve diagnostic accuracy, predict patient outcomes (including mortality and functional recovery), and potentially guide treatment decisions, such as surgical intervention, leading to better patient care and improved clinical management of ICH.