  In this paper, we introduce a new dataset in the medical field of
hypertensive intracerebral hemorrhage (HICH), called HICH-IT, which includes
both electronic medical records (EMRs) and head CT images. This dataset is
designed to enhance the accuracy of artificial intelligence in the diagnosis
and treatment of HICH. This dataset, built upon the foundation of standard text
and image data, incorporates specific annotations within the EMRs, extracting
key content from the text information, and categorizes the annotation content
of imaging data into four types: brain midline, hematoma, left and right
cerebral ventricle. HICH-IT aims to be a foundational dataset for feature
learning in image segmentation tasks and named entity recognition. To further
understand the dataset, we have trained deep learning algorithms to observe the
performance. The pretrained models have been released at both www.daip.club and
github.com/Deep-AI-Application-DAIP. The dataset has been uploaded to
https://github.com/CYBUS123456/HICH-IT-Datasets.
  Index Terms-HICH, Deep learning, Intraparenchymal hemorrhage, named entity
recognition, novel dataset
