In this paper, we introduce a new multimodal dataset in the medical field of
hypertensive intracerebral hemorrhage(HICH), called as HICH-IT, which includes
both textual information 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 text data, extracting key
content from the text information, and categorizes the annotation content of
imaging data into four types: brain midline, hematoma, left cerebral ventricle,
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 this https URL
Index Terms-HICH, Deep learning, Intraparenchymal hemorrhage, named entity
recognition, novel dataset

介绍了一种名为 HICH-IT 的新的医学领域高血压脑内出血（HICH）的多模态数据集，旨在提高人工智能在 HICH 的诊断和治疗中的准确性。该数据集包含文本信息和头部 CT 图像，并结合特定注释和深度学习算法，用于图像分割和命名实体识别的特征学习。