Dual-Domain Cross-Modal Decoding for Clinical Text-Guided Medical Image Segmentation
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 feat
Record details
Published: 11 August 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 13 August 2026
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ethics.ai (11 August 2026), “Dual-Domain Cross-Modal Decoding for Clinical Text-Guided Medical Image Segmentation,” evidence record 18812, https://ethics.ai/record/18812 (originally published by arXiv).
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