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Multi-region saliency-aware learning for cross-domain placenta image segmentation
Pattern Recognition Letters ( IF 5.1 ) Pub Date : 2020-10-10 , DOI: 10.1016/j.patrec.2020.10.004
Zhuomin Zhang , Dolzodmaa Davaasuren , Chenyan Wu , Jeffery A. Goldstein , Alison D. Gernand , James Z. Wang

We propose a multi-region saliency-aware learning (MSL) method for cross-domain placenta image segmentation. Unlike most existing image-level transfer learning methods that fail to preserve the semantics of paired regions, our MSL incorporates the attention mechanism and a saliency constraint into the adversarial translation process, which can realize multi-region mappings in the semantic level. Specifically, the built-in attention module serves to detect the most discriminative semantic regions that the generator should focus on. Then we use the attention consistency as another guidance for retaining semantics after translation. Furthermore, we exploit the specially designed saliency-consistent constraint to enforce the semantic consistency by requiring the saliency regions unchanged. We conduct experiments using two real-world placenta datasets we have collected. We examine the efficacy of this approach in (1) segmentation and (2) prediction of the placental diagnoses of fetal and maternal inflammatory response (FIR, MIR). Experimental results show the superiority of the proposed approach over the state of the art.



中文翻译:

跨域胎盘图像分割的多区域显着性学习

我们提出了一种用于跨域胎盘图像分割的多区域显着性学习(MSL)方法。与大多数现有的图像级迁移学习方法无法保留配对区域的语义不同,我们的MSL将注意力机制和显着性约束纳入对抗性翻译过程中,从而可以在语义级别实现多区域映射。具体而言,内置的关注模块用于检测生成器应关注的最具区分性的语义区域。然后,我们将注意力一致性用作翻译后保留语义的另一种指导。此外,我们利用特殊设计的显着性一致性约束来通过要求显着性区域保持不变来增强语义一致性。我们使用已收集的两个真实世界的胎盘数据集进行实验。我们在(1)分割和(2)胎盘诊断胎儿和母亲的炎症反应(FIR,MIR)中检查了这种方法的功效。实验结果表明,所提出的方法优于现有技术。

更新日期:2020-10-17
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