自监督学习生成可变原型，提升医学图像分割精度
Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning
- 为每张图像定制特定原型，融合通用与个体特征
- 通过置信度重加权增强类内差异建模能力
- 适合需要高精度分割的医疗影像分析场景
医学图像分割在治疗规划与机器人导航中至关重要。现有原型学习方法通常对每个语义类别使用固定原型，忽视样本多样性及输入内部的类内差异。本文提出实例自适应原型学习，结合捕捉通用视觉模式的公共原型提议（CPP）与针对每张输入定制的实例特定原型提议（IPP）。为建模类内变化，利用基于变压器解码器分层生成的置信度分数对中间特征图进行重加权。此外，引入一种新型自监督过滤策略，在变压器解码器训练中优先关注前景像素。大量实验表明该方法表现优异。
原文摘要 · Abstract (English)
Medical Image Segmentation (MIS) plays a crucial role in medical therapy planning and robot navigation. Prototype learning methods in MIS focus on generating segmentation masks through pixel-to-prototype comparison. However, current approaches often overlook sample diversity by using a fixed prototype per semantic class and neglect intra-class variation within each input. In this paper, we propose to generate instance-adaptive prototypes for MIS, which integrates a common prototype proposal (CPP) capturing common visual patterns and an instance-specific prototype proposal (IPP) tailored to each input. To further account for the intra-class variation, we propose to guide the IPP generation by re-weighting the intermediate feature map according to their confidence scores. These confidence scores are hierarchically generated using a transformer decoder. Additionally we introduce a novel self-supervised filtering strategy to prioritize the foreground pixels during the training of the transformer decoder. Extensive experiments demonstrate favorable performance of our method.
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