An end-to-end deep learning pipeline for hematoma expansion prediction in spontaneous intracerebral hemorrhage based on non-contrast computed tomography
Qiang Yu, Xin Fan, Jinwei Li, Qianyu Hao, Youquan Ning, Shichao Long, Wenhao Jiang, Fajin Lv, Xianlei Yan, Quan Liu, Xiaoquan Xu, Zongqian Wu, Juan Peng, Min Wu · 2025
Read An end-to-end deep learning pipeline for hematoma expansion prediction in spontaneous intracerebral hemorrhage based on non-contrast computed tomography by Qiang Yu, Xin Fan, Jinwei Li, Qianyu Hao, Youquan Ning, Shichao Long, Wenhao Jiang, Fajin Lv, Xianlei Yan, Quan Liu, Xiaoquan Xu, Zongqian Wu, Juan Peng, Min Wu (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12800295.txt.
Description
Qiang Yu and co-authors build in 2025 an automated tool that forecasts whether bleeding in the brain will grow. Such growth, called hematoma expansion, is a key target in the first hours after a bleed from a burst vessel. Spotting it early from plain scans is hard, yet it shapes urgent care choices.
Their pipeline joins three parts: an automatic outline of the bleed, synthetic extra training images, and a vision transformer that sorts cases. It learned from 2,020 patients at five centres and held up well on outside data from other hospitals. The authors offer it as a front-line aid for fast risk sorting in acute care.
Catalog record
| CREATOR | Qiang Yu, Xin Fan, Jinwei Li, Qianyu Hao, Youquan Ning, Shichao Long, Wenhao Jiang, Fajin Lv, Xianlei Yan, Quan Liu, Xiaoquan Xu, Zongqian Wu, Juan Peng, Min Wu |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 51 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |