  Medical reports with substantial information can be naturally complementary
to medical images for computer vision tasks, and the modality gap between
vision and language can be solved by vision-language matching (VLM). However,
current vision-language models distort the intra-model relation and mainly
include class information in prompt learning that is insufficient for
segmentation task. In this paper, we introduce a Bi-level class-severity-aware
Vision-Language Graph Matching (Bi-VLGM) for text guided medical image
segmentation, composed of a word-level VLGM module and a sentence-level VLGM
module, to exploit the class-severity-aware relation among visual-textual
features. In word-level VLGM, to mitigate the distorted intra-modal relation
during VLM, we reformulate VLM as graph matching problem and introduce a
vision-language graph matching (VLGM) to exploit the high-order relation among
visual-textual features. Then, we perform VLGM between the local features for
each class region and class-aware prompts to bridge their gap. In
sentence-level VLGM, to provide disease severity information for segmentation
task, we introduce a severity-aware prompting to quantify the severity level of
retinal lesion, and perform VLGM between the global features and the
severity-aware prompts. By exploiting the relation between the local (global)
and class (severity) features, the segmentation model can selectively learn the
class-aware and severity-aware information to promote performance. Extensive
experiments prove the effectiveness of our method and its superiority to
existing methods. Source code is to be released.
