# Brain Hematoma Marker Recognition Using Multitask Learning: SwinTransformer and Swin-Unet

Source: [Brain Hematoma Marker Recognition Using Multitask Learning: SwinTransformer and Swin-Unet](<https://www.sota2.com/research/paper/68fa78881e209131cea78e78>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:52:53.434Z

Authors: Kodai Hirata, Tsuyoshi Okita

Published: 2025-05-09T16:54:26.000Z

## Abstract

This paper proposes a method MTL-Swin-Unet which is multi-task learning using transformers for classification and semantic segmentation. For spurious-correlation problems, this method allows us to enhance the image representation with two other image representations: representation obtained by semantic segmentation and representation obtained by image reconstruction. In our experiments, the proposed method outperformed in F-value measure than other classifiers when the test data included slices from the same patient (no covariate shift). Similarly, when the test data did not include slices from the same patient (covariate shift setting), the proposed method outperformed in AUC measure.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2505.06185>)

[PDF](<https://arxiv.org/pdf/2505.06185.pdf>)