Diabetic foot ulcers pose health risks, including higher morbidity,
mortality, and amputation rates. Monitoring wound areas is crucial for proper
care, but manual segmentation is subjective due to complex wound features and
background variation. Expert annotations are costly and time-intensive, thus
hampering large dataset creation. Existing segmentation models relying on
extensive annotations are impractical in real-world scenarios with limited
annotated data. In this paper, we propose a cross-domain augmentation method
named TransMix that combines Augmented Global Pre-training AGP and Localized
CutMix Fine-tuning LCF to enrich wound segmentation data for model learning.
TransMix can effectively improve the foot ulcer segmentation model training by
leveraging other dermatology datasets not on ulcer skins or wounds. AGP
effectively increases the overall image variability, while LCF increases the
diversity of wound regions. Experimental results show that TransMix increases
the variability of wound regions and substantially improves the Dice score for
models trained with only 40 annotated images under various proportions.

提出了一种名为 TransMix 的跨域增强方法，通过结合 AGP 和 LCF 来丰富足溃疡分割数据，从而有效改善模型训练；实验证明，TransMix 可以提高足溃疡区域的变异性并显著提高只用 40 个注释图像训练的模型的 Dice 评分。