Intracranial Hemorrhage Detection

Intracranial hemorrhage (ICH) detection aims to accurately identify bleeding within the brain using medical imaging, primarily CT scans, to facilitate timely diagnosis and treatment. Current research heavily utilizes deep learning, employing various architectures such as convolutional neural networks (CNNs), transformers, and multiple instance learning (MIL) models, often incorporating attention mechanisms to improve feature extraction and localization of hemorrhages. These advancements focus on improving diagnostic accuracy, addressing challenges like limited data availability and inter-observer variability, and enhancing the trustworthiness of AI-driven detection systems through techniques like conformal prediction. Ultimately, improved ICH detection methods promise to aid radiologists in faster and more accurate diagnosis, leading to better patient outcomes.