聚焦弱特征生成硬原型，提升少样本医学图像分割边界清晰度。
Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation
- 通过自预测模块识别支持图像中的弱特征，定位关键边界信息。
- 基于弱特征生成多个硬原型，显著减少分割边界模糊问题。
- 双路径融合与边界损失设计，适合小样本医学图像精细分割任务。
少样本医学图像分割（FSMIS）旨在仅用少量标注图像训练模型完成分割。然而，现有基于原型的方法多通过随机采样或局部平均生成原型，易因正常特征占主导而造成严重边界模糊。为此，本文提出聚焦弱特征：设计支持自预测（SSP）模块，通过全局原型预测与真实掩码对比，识别关键弱特征；再通过硬原型生成（HPG）模块构建多个硬原型。随后，采用双路径相似图融合（MSMF）缓解医学图像中前景与背景的不平衡问题，并引入边界损失进一步约束分割边缘。在三个公开医学数据集上的实验表明，该方法达到当前最优性能。代码已开源：https://github.com/jcjiang99/CoW。
原文摘要 · Abstract (English)
Few-Shot Medical Image Segmentation (FSMIS) has been widely used to train a model that can perform segmentation from only a few annotated images. However, most existing prototype-based FSMIS methods generate multiple prototypes from the support image solely by random sampling or local averaging, which can cause particularly severe boundary blurring due to the tendency for normal features accounting for the majority of features of a specific category. Consequently, we propose to focus more attention to those weaker features that are crucial for clear segmentation boundary. Specifically, we design a Support Self-Prediction (SSP) module to identify such weak features by comparing true support mask with one predicted by global support prototype. Then, a Hard Prototypes Generation (HPG) module is employed to generate multiple hard prototypes based on these weak features. Subsequently, a Multiple Similarity Maps Fusion (MSMF) module is devised to generate final segmenting mask in a dual-path fashion to mitigate the imbalance between foreground and background in medical images. Furthermore, we introduce a boundary loss to further constraint the edge of segmentation. Extensive experiments on three publicly available medical image datasets demonstrate that our method achieves state-of-the-art performance. Code is available at https://github.com/jcjiang99/CoW.
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