AKGNet: Attribute Knowledge-Guided Unsupervised Lung-Infected Area
  Segmentation
Qing En Yuhong Guo
Lung-infected area segmentation is crucial for assessing the severity of lung
diseases. However, existing image-text multi-modal methods typically rely on
labour-intensive annotations for model training, posing challenges regarding
time and expertise. To address this issue, we propose a novel attribute
knowledge-guided framework for unsupervised lung-infected area segmentation
(AKGNet), which achieves segmentation solely based on image-text data without
any mask annotation. AKGNet facilitates text attribute knowledge learning,
attribute-image cross-attention fusion, and high-confidence-based pseudo-label
exploration simultaneously. It can learn statistical information and capture
spatial correlations between image and text attributes in the embedding space,
iteratively refining the mask to enhance segmentation. Specifically, we
introduce a text attribute knowledge learning module by extracting attribute
knowledge and incorporating it into feature representations, enabling the model
to learn statistical information and adapt to different attributes. Moreover,
we devise an attribute-image cross-attention module by calculating the
correlation between attributes and images in the embedding space to capture
spatial dependency information, thus selectively focusing on relevant regions
while filtering irrelevant areas. Finally, a self-training mask improvement
process is employed by generating pseudo-labels using high-confidence
predictions to iteratively enhance the mask and segmentation. Experimental
results on a benchmark medical image dataset demonstrate the superior
performance of our method compared to state-of-the-art segmentation techniques
in unsupervised scenarios.