Pulmonary Embolisms (PE) are a leading cause of cardiovascular death. Computed tomographic pulmonary angiography (CTPA) stands as the gold standard for diagnosing pulmonary embolisms (PE) and there has been a lot of interest in developing AI-based models for assisting in PE diagnosis. Performance of these algorithms has been hindered by the scarcity of annotated data, especially those with fine-grained delineation of the thromboembolic burden. In this paper we attempt to address this issue by introducing a weakly supervised learning pipeline, that leverages model explainability to generate fine-grained (pixel level) masks for embolisms starting from more coarse-grained (binary, image level) PE annotations. Furthermore, we show that training models using the automatically generated pixel annotations yields good PE localization performance. We demonstrate the effectiveness of our pipeline on the large-scale, multi-center RSPECT augmented dataset for PE detection and localization.

本研究解决了肺栓塞（PE）诊断中缺乏精细标注数据的问题。通过提出一种弱监督学习方法，该方法利用模型可解释性从图像级的粗略注释生成精细的像素级掩膜，训练出的模型展示出良好的肺栓塞定位性能。我们在大规模的多中心RSPECT增强数据集上验证了该方法的有效性。

通过模型可解释性从图像级注释学习肺栓塞分割