Report
SGSeg: Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidance
| العنوان: | SGSeg: Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidance |
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| المؤلفون: | Ye, Shuchang, Meng, Mingyuan, Li, Mingjian, Feng, Dagan, Kim, Jinman |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: This preprint has not undergone peer review or any post-submission improvments or corrections |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/978-3-031-72111-3_23 |
| URL الوصول: | http://arxiv.org/abs/2409.04758 |
| رقم الانضمام: | edsarx.2409.04758 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/978-3-031-72111-3_23 |
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