SGSeg: Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidance
<|reference_start|>SGSeg: Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidance: Segmentation of infected areas in chest X-rays is pivotal for facilitating the accurate delineation of pulmonary structures and pathological anomalies. Recently, multi-modal language-guided image segmentation methods have emerged as a promising solution for chest X-rays where the clinical text reports, depicting the assessment of the images, are used as guidance. Nevertheless, existing language-guided methods require clinical reports alongside the images, and hence, they are not applicable for use in image segmentation in a decision support context, but rather limited to retrospective image analysis after clinical reporting has been completed. In this study, we propose a self-guided segmentation framework (SGSeg) that leverages language guidance for training (multi-modal) while enabling text-free inference (uni-modal), which is the first that enables text-free inference in language-guided segmentation. We exploit the critical location information of both pulmonary and pathological structures depicted in the text reports and introduce a novel localization-enhanced report generation (LERG) module to generate clinical reports for self-guidance. Our LERG integrates an object detector and a location-based attention aggregator, weakly-supervised by a location-aware pseudo-label extraction module. Extensive experiments on a well-benchmarked QaTa-COV19 dataset demonstrate that our SGSeg achieved superior performance than existing uni-modal segmentation methods and closely matched the state-of-the-art performance of multi-modal language-guided segmentation methods.<|reference_end|>
@article{ye2024sgseg:,
    title={SGSeg: Enabling Text-free Inference in Language-guided Segmentation of
  Chest X-rays via Self-guidance},
    author={Shuchang Ye, Mingyuan Meng, Mingjian Li, Dagan Feng, Jinman Kim},
    journal={arXiv preprint arXiv:2409.04758},
    year={2024},
    doi={10.1007/978-3-031-72111-3_23},
    archivePrefix={arXiv},
    eprint={2409.04758},
    primaryClass={cs.CV}
}