Style Augmentation improves Medical Image Segmentation
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Abstract (arXiv)
Due to the limitation of available labeled data, medical image segmentation is a challenging task for deep learning. Traditional data augmentation techniques have been shown to improve segmentation network performances by optimizing the usage of few training examples. However, current augmentation approaches for segmentation do not tackle the strong texture bias of convolutional neural networks, observed in several studies. This work shows on the MoNuSeg dataset that style augmentation, which is already used in classification tasks, helps reducing texture over-fitting and improves segmentation performance.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 11 references that are in the map. First posted on arXiv 2 Nov 2022.
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6 of 11 references.
References · 6
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,432
- 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 320
- 2018Style Augmentation: Data Augmentation via Style Randomizationcited by 79
- 2021SinGAN-Seg: Synthetic training data generation for medical image segmentationcited by 43
- 2020On the Texture Bias for Few-Shot CNN Segmentationcited by 38
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