# Radiologist-level stroke classification on non-contrast CT scans with Deep U-Net

arXiv: https://arxiv.org/abs/2003.14287 · HTML: https://mlsanity.com/paper/2003.14287/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [Small Lesion Segmentation in Brain MRIs with Subpixel Embedding](https://mlsanity.com/paper/2109.08791/) (2021)
- [CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning](https://mlsanity.com/paper/2105.11863/) (2021)
- [Optimizing the Procedure of CT Segmentation Labeling](https://mlsanity.com/paper/2303.14089/) (2023)

## Abstract (arXiv)

Segmentation of ischemic stroke and intracranial hemorrhage on computed tomography is essential for investigation and treatment of stroke. In this paper, we modified the U-Net CNN architecture for the stroke identification problem using non-contrast CT. We applied the proposed DL model to historical patient data and also conducted clinical experiments involving ten experienced radiologists. Our model achieved strong results on historical data, and significantly outperformed seven radiologist out of ten, while being on par with the remaining three.