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

TL;DR

This paper introduces MTL-Swin-Unet, a multi-task transformer-based model for brain hematoma detection that improves classification and segmentation performance, especially under covariate shift conditions.

Contribution

The paper presents a novel multi-task learning approach using transformers that enhances image representation by integrating segmentation and reconstruction tasks for better hematoma recognition.

Findings

Outperformed other classifiers in F-value with same-patient test data.

Achieved higher AUC in covariate shift scenarios.

Demonstrated robustness of the method across different test conditions.

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.