用共享原型提升医学图像少样本分割精度
Tied Prototype Model for Few-Shot Medical Image Segmentation
- 将前景与背景原型位置绑定，统一建模分布
- 支持多原型和多类别，准确率显著提升
- 自适应阈值结合先验知识，适合临床应用
现有基于原型的医学图像少样本分割方法通常为前景和背景分别建模。但鉴于背景变化大，更优策略是仅建模前景，将背景视为异常——此思路由ADNet提出。然而，ADNet存在三大缺陷：每类依赖单一原型、仅支持二分类、固定阈值无法适应患者与器官差异。为此，本文提出系联原型模型（TPM），在概率框架下对ADNet进行原则性重构，使前景与背景原型位置绑定。基于此，TPM自然扩展至多原型和多类别分割，有效分离非典型背景特征，两项改进均提升分割准确率。此外，利用自然存在的类别先验定义理想目标，实现自适应阈值，进一步提升性能。总体上，TPM为医学图像少样本分割提供了新视角。代码已开源：https://github.com/hjk92g/TPM-FSS。
原文摘要 · Abstract (English)
Common prototype-based medical image few-shot segmentation (FSS) methods model foreground and background classes using class-specific prototypes. However, given the high variability of the background, a more promising direction is to focus solely on foreground modeling, treating the background as an anomaly -- an approach introduced by ADNet. Yet, ADNet faces three key limitations: dependence on a single prototype per class, a focus on binary classification, and fixed thresholds that fail to adapt to patient and organ variability. To address these shortcomings, we propose the Tied Prototype Model (TPM), a principled reformulation of ADNet with tied prototype locations for foreground and background distributions. Building on its probabilistic foundation, TPM naturally extends to multiple prototypes and multi-class segmentation while effectively separating non-typical background features. Notably, both extensions lead to improved segmentation accuracy. Finally, we leverage naturally occurring class priors to define an ideal target for adaptive thresholds, boosting segmentation performance. Taken together, TPM provides a fresh perspective on prototype-based FSS for medical image segmentation. The code can be found at https://github.com/hjk92g/TPM-FSS.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。