用多任务Transformer提升脑出血标记识别准确率
Brain Hematoma Marker Recognition Using Multitask Learning: SwinTransformer and Swin-Unet
- 采用SwinTransformer实现分类与语义分割联合学习
- 在同患者数据上F值优于现有方法，在跨患者数据上AUC更优
- 适合医学图像分析、需要抗数据偏移的场景
本文提出一种基于多任务学习的SwinTransformer框架MTL-Swin-Unet，用于脑出血标记的分类与语义分割。针对虚假相关性问题，该方法通过引入语义分割和图像重建所得的特征表示，增强原始图像表征能力。实验表明，在测试数据包含同一患者切片（无协变量偏移）时，该方法在F值指标上优于其他分类器；当测试数据不包含同一患者切片（存在协变量偏移）时，其在AUC指标上表现更优。
原文摘要 · Abstract (English)
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.
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