用文本提示和语义蒸馏提升医学图像分割精度，减少对精细标注依赖。
TAMISeg: Text-Aligned Multi-scale Medical Image Segmentation with Semantic Encoder Distillation
- 引入文本提示与语义蒸馏作为辅助语义线索，增强模型理解能力。
- 在三个医学数据集上均超越现有单模态与多模态方法，提升显著。
- 适合医学图像分割研究者，尤其关注弱监督与跨模态学习的场景。
医学图像分割因标注信息有限、解剖结构复杂以及噪声、低对比度或光照变化导致的图像退化而面临挑战。本文提出TAMISeg，一种结合临床语言提示和语义蒸馏的文本引导分割框架，以增强视觉理解并降低对像素级细粒度标注的依赖。该框架包含三个核心组件：一个在强扰动下预训练的鲁棒特征提取编码器；一个利用冻结的DINOv3教师模型监督的语义蒸馏模块，提升语义判别能力；以及一个自适应多尺度解码器，可实现不同空间尺度上的解剖结构分割。在Kvasir-SEG、MosMedData+和QaTa-COV19数据集上的实验表明，TAMISeg在定性和定量评估中均持续优于现有单模态与多模态方法。代码将公开于https://github.com/qczggaoqiang/TAMISeg。
原文摘要 · Abstract (English)
Medical image segmentation remains challenging due to limited fine-grained annotations, complex anatomical structures, and image degradation from noise, low contrast, or illumination variation. We propose TAMISeg, a text-guided segmentation framework that incorporates clinical language prompts and semantic distillation as auxiliary semantic cues to enhance visual understanding and reduce reliance on pixel-level fine-grained annotations. TAMISeg integrates three core components: a consistency-aware encoder pretrained with strong perturbations for robust feature extraction, a semantic encoder distillation module with supervision from a frozen DINOv3 teacher to enhance semantic discriminability, and a scale-adaptive decoder that segments anatomical structures across different spatial scales. Experiments on the Kvasir-SEG, MosMedData+, and QaTa-COV19 datasets demonstrate that TAMISeg consistently outperforms existing uni-modal and multi-modal methods in both qualitative and quantitative evaluations. Code will be made publicly available at https://github.com/qczggaoqiang/TAMISeg.
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