用逻辑规则提升医学图像分割精度，尤其在数据少时效果更明显
Integrating Background Knowledge in Medical Semantic Segmentation with Logic Tensor Networks
- 用一阶逻辑规则编码医学常识，融入分割模型损失函数
- 在脑部MRI数据上，小样本下分割准确率显著提升
- 适合医疗图像标注稀缺场景，可推广至其他器官分割任务
语义分割是医学图像分析中的基础任务，有助于放射科医生区分图像中的结构。深度学习推动了该领域的发展，但在噪声和伪影存在时仍不完善。本文提出将医学常识融入分割模型的损失函数以提升性能。通过逻辑张量网络（LTNs）使用一阶逻辑（FOL）规则编码知识，涵盖分割结果形状约束及不同区域间关系。构建基于SwinUNETR的端到端框架，在脑部MRI的海马体分割任务上验证方法有效性。实验表明，当训练数据稀缺时，引入LTNs能显著提升分割表现。尽管仍处初期，但神经符号方法具有通用性，可拓展至其他医学分割任务。
原文摘要 · Abstract (English)
Semantic segmentation is a fundamental task in medical image analysis, aiding medical decision-making by helping radiologists distinguish objects in an image. Research in this field has been driven by deep learning applications, which have the potential to scale these systems even in the presence of noise and artifacts. However, these systems are not yet perfected. We argue that performance can be improved by incorporating common medical knowledge into the segmentation model's loss function. To this end, we introduce Logic Tensor Networks (LTNs) to encode medical background knowledge using first-order logic (FOL) rules. The encoded rules span from constraints on the shape of the produced segmentation, to relationships between different segmented areas. We apply LTNs in an end-to-end framework with a SwinUNETR for semantic segmentation. We evaluate our method on the task of segmenting the hippocampus in brain MRI scans. Our experiments show that LTNs improve the baseline segmentation performance, especially when training data is scarce. Despite being in its preliminary stages, we argue that neurosymbolic methods are general enough to be adapted and applied to other medical semantic segmentation tasks.
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