The human body shows elements of bilateral symmetry for various body parts, including the lung. This symmetry can be disturbed by a variety of diseases or abnormalities, e.g. by lung diseases such as pneumonia. While radiologists use lung field symmetry information in their radiological examinations to analyze chest X-rays, it is still underutilized in the field of computer vision.<\/p>\n
To investigate the potential of pathologically induced asymmetry of the lung field for the automatic detection of healthy and diseased patients, we implement a symmetry-aware architecture. The model is based on a Siamese network with a DenseNet backbone and a symmetry-aware contrastive loss function. Two different processing pipelines are investigated: first, the scan is processed as a whole image, and second, the left and right lung fields are separated. This enables an independent determination of the most important features of each lung field.<\/p>\n
Compared to state-of-the-art baseline models (DenseNet, Mask R-CNN), symmetry-aware training can improve the AUROC score by up to 10\\%. Furthermore, the findings indicate that, by integrating the bilateral symmetry of the lung field, the interpretability of the models increases. The generated probability maps show a stronger focus on lung field and disease features compared to state-of-the-art algorithms like Grad-Cam++ for heat map generation or Mask R-CNN for object detection.<\/p>\n","protected":false},"excerpt":{"rendered":"
The human body shows elements of bilateral symmetry for various body parts, including the lung. This symmetry can be disturbed by a variety of diseases or abnormalities, e.g. by lung diseases such as pneumonia. While radiologists use lung field symmetry information in their radiological examinations to analyze chest X-rays, it is still underutilized in the field of computer vision. To investigate the potential of pathologically induced asymmetry of the lung […]<\/p>\n","protected":false},"author":14,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-32914","publication","type-publication","status-publish","hentry","publication-type-inproceedings"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32914","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/14"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32914\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32914"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32914"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}