تقرير
Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation
العنوان: | Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation |
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المؤلفون: | Chen, Hao, Zhang, Hongrun, Chan, U Wang, Yin, Rui, Wang, Xiaofei, Li, Chao |
سنة النشر: | 2024 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
الوصف: | Single domain generalization aims to address the challenge of out-of-distribution generalization problem with only one source domain available. Feature distanglement is a classic solution to this purpose, where the extracted task-related feature is presumed to be resilient to domain shift. However, the absence of references from other domains in a single-domain scenario poses significant uncertainty in feature disentanglement (ill-posedness). In this paper, we propose a new framework, named \textit{Domain Game}, to perform better feature distangling for medical image segmentation, based on the observation that diagnostic relevant features are more sensitive to geometric transformations, whilist domain-specific features probably will remain invariant to such operations. In domain game, a set of randomly transformed images derived from a singular source image is strategically encoded into two separate feature sets to represent diagnostic features and domain-specific features, respectively, and we apply forces to pull or repel them in the feature space, accordingly. Results from cross-site test domain evaluation showcase approximately an ~11.8% performance boost in prostate segmentation and around ~10.5% in brain tumor segmentation compared to the second-best method. |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2406.02125 |
رقم الأكسشن: | edsarx.2406.02125 |
قاعدة البيانات: | arXiv |
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