AKGNet: Attribute Knowledge-Guided Unsupervised Lung-Infected Area Segmentation

arXiv:2404.11008v1 Announce Type: new
Abstract: 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 …

abstract annotations arxiv challenges cs.ai cs.cv diseases expertise framework however image issue knowledge labour modal multi-modal novel segmentation text training type unsupervised