# An Effective Transformer-based Solution for RSNA Intracranial Hemorrhage Detection Competition

arXiv: https://arxiv.org/abs/2205.07556 · HTML: https://mlsanity.com/paper/2205.07556/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 16 May 2022.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans](https://mlsanity.com/paper/1803.05854/) (2018)

## Abstract (arXiv)

We present an effective method for Intracranial Hemorrhage Detection (IHD) which exceeds the performance of the winner solution in RSNA-IHD competition (2019). Meanwhile, our model only takes quarter parameters and ten percent FLOPs compared to the winner's solution. The IHD task needs to predict the hemorrhage category of each slice for the input brain CT. We review the top-5 solutions for the IHD competition held by the Radiological Society of North America(RSNA) in 2019. Nearly all the top solutions rely on 2D convolutional networks and sequential models (Bidirectional GRU or LSTM) to extract intra-slice and inter-slice features, respectively. All the top solutions enhance the performance by leveraging the model ensemble, and the model number varies from 7 to 31. In the past years, since much progress has been made in the computer vision regime especially Transformer-based models, we introduce the Transformer-based techniques to extract the features in both intra-slice and inter-slice views for IHD tasks. Additionally, a semi-supervised method is embedded into our workflow to further improve the performance. The code is available in the manuscript.