# HICH Image/Text (HICH-IT): Comprehensive Text and Image Datasets for Hypertensive Intracerebral Hemorrhage Research

arXiv: https://arxiv.org/abs/2401.15934 · HTML: https://mlsanity.com/paper/2401.15934/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [An overview of deep learning in medical imaging focusing on MRI](https://mlsanity.com/paper/1811.10052/) (2018)

## Abstract (arXiv)

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