  Recent developments underscore the potential of textual information in
enhancing learning models for a deeper understanding of medical visual
semantics. However, language-guided medical image segmentation still faces a
challenging issue. Previous works employ implicit and ambiguous architectures
to embed textual information. This leads to segmentation results that are
inconsistent with the semantics represented by the language, sometimes even
diverging significantly. To this end, we propose a novel cross-modal
conditioned Reconstruction for Language-guided Medical Image Segmentation
(RecLMIS) to explicitly capture cross-modal interactions, which assumes that
well-aligned medical visual features and medical notes can effectively
reconstruct each other. We introduce conditioned interaction to adaptively
predict patches and words of interest. Subsequently, they are utilized as
conditioning factors for mutual reconstruction to align with regions described
in the medical notes. Extensive experiments demonstrate the superiority of our
RecLMIS, surpassing LViT by 3.74% mIoU on the publicly available MosMedData+
dataset and achieving an average increase of 1.89% mIoU for cross-domain tests
on our QATA-CoV19 dataset. Simultaneously, we achieve a relative reduction of
20.2% in parameter count and a 55.5% decrease in computational load. The code
will be available at https://github.com/ShashankHuang/RecLMIS.
