用文本指导肺部感染分割，同时关注空间与频率信息。
Dual-Domain Cross-Modal Decoding for Clinical Text-Guided Medical Image Segmentation
- 在空间和频率双域融合文本信息，提升图像分割精度。
- 在两个数据集上分别达到91.46% Dice、81.95% Dice的性能。
- 适合医学影像分析与多模态融合研究者参考。
临床文本可缩小需分割的范围，但现有文本引导方法侧重空间对齐，忽视影响纹理与边界的频域特征。本文提出双域跨模态解码（DD-CMD）用于临床文本引导的肺部感染分割，在解码过程中整合两种互补的语言引导机制。在空间域，文本引导空间交叉注意力（TGSA）将多尺度视觉标记与文本语义对齐，并通过门控残差融合更新特征。在频率域，频谱-文本自适应调制（STAM）采用二维DCT计算可学习的带能量统计量，预测文本条件下的FiLM参数，以实现频域感知的解码。DD-CMD将TGSA与STAM嵌入从7x7到56x56的粗到细解码器中，并通过轻量级两阶段细化模块恢复全分辨率掩码。在QaTa-COV19和MosMedData+数据集上的实验表明，其分别取得91.46% Dice / 84.26% mIoU和81.95% Dice / 69.42% mIoU的成绩，平均优于最强基线+1.96 Dice和+2.67 mIoU。
原文摘要 · Abstract (English)
Clinical text can narrow down what to segment, but recent text-guided designs emphasize spatial alignment while overlooking frequency content that governs texture and boundaries. We propose Dual-Domain Cross-Modal Decoding (DD-CMD) for clinical text-guided pulmonary infection segmentation, integrating two complementary forms of language guidance during decoding. In the spatial domain, Text-Guided Spatial Cross-Attention (TGSA) aligns multi-scale visual tokens with text semantics and updates features through gated residual fusion. In the frequency domain, Spectral-Text Adaptive Modulation (STAM) applies a 2D DCT to compute learnable band-energy statistics and predicts text-conditioned FiLM parameters to recalibrate decoder channels for frequency-aware decoding. DD-CMD embeds TGSA and STAM into a coarse-to-fine decoder (7x7 to 56x56) and restores full-resolution masks using a lightweight two-stage refinement module. Experiments on QaTa-COV19 and MosMedData+ show that DD-CMD achieves 91.46% Dice / 84.26% mIoU and 81.95% Dice / 69.42% mIoU, respectively, with average gains of +1.96 Dice and +2.67 mIoU over the strongest prior baselines. Code: https://github.com/maklachur/DD-CMD.
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