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.

Dual-modal Dynamic Traceback Learning for Medical Report Generation

With increasing reliance on medical imaging in clinical practices, automated report generation from medical images is in great demand. Existing report generation methods typically adopt an encoder-decoder deep learning framework to build a uni-directional image-to-report mapping. However, such a framework ignores the bi-directional mutual associations between images and reports, thus incurring difficulties in associating the intrinsic medical meanings between them. Recent generative representation learning methods have demonstrated the benefits of dual-modal learning from both image and text modalities. However, these methods exhibit two major drawbacks for medical report generation: 1) they tend to capture morphological information and have difficulties in capturing subtle pathological semantic information, and 2) they predict masked text rely on both unmasked images and text, inevitably degrading performance when inference is based solely on images. In this study, we propose a new report generation framework with dual-modal dynamic traceback learning (DTrace) to overcome the two identified drawbacks and enable dual-modal learning for medical report generation. To achieve this, our DTrace introduces a traceback mechanism to control the semantic validity of generated content via self-assessment. Further, our DTrace introduces a dynamic learning strategy to adapt to various proportions of image and text input, enabling report generation without reliance on textual input during inference. Extensive experiments on two well-benchmarked datasets (IU-Xray and MIMIC-CXR) show that our DTrace outperforms state-of-the-art medical report generation methods.