{"id":5859,"date":"2025-02-13T12:59:39","date_gmt":"2025-02-13T11:59:39","guid":{"rendered":"https:\/\/dbis.rwth-aachen.de\/dbis\/?p=5859"},"modified":"2025-04-16T10:13:03","modified_gmt":"2025-04-16T08:13:03","slug":"knowledge-graph-enhanced-vision-language-models-for-radiology-report-generation","status":"publish","type":"post","link":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/2025\/knowledge-graph-enhanced-vision-language-models-for-radiology-report-generation\/","title":{"rendered":"Knowledge Graph-Enhanced Vision-Language Models for Radiology Report Generation"},"content":{"rendered":"\n<p>Radiology report generation is a critical task in medical imaging analysis, where accurate and comprehensive descriptions of medical scans (such as X-ray, CT, or MRI) are required for diagnosis and treatment planning. Vision-language models (VLMs) have recently gained attention for automating this process by generating textual reports from medical images. However, standard VLMs often suffer from factual inconsistencies, limited domain knowledge, and difficulties in handling complex medical terminology. Knowledge graphs (KGs) provide structured domain-specific information, offering an opportunity to enhance VLMs with prior medical knowledge. Integrating knowledge graphs into vision-language models can improve the accuracy and interpretability of generated radiology reports by ensuring consistency with known medical facts and terminology. This thesis investigates how knowledge graph-enhanced VLMs can improve the quality, factual correctness, and clinical relevance of automated radiology report generation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Radiology report generation is a critical task in medical imaging analysis, where accurate and comprehensive descriptions of medical scans (such as X-ray, CT, or MRI) are required for diagnosis and treatment planning. Vision-language models (VLMs) have recently gained attention for automating this process by generating textual reports from medical images. However, standard VLMs often suffer [&hellip;]<\/p>\n","protected":false},"author":19,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[21],"tags":[],"class_list":["post-5859","post","type-post","status-publish","format-standard","hentry","category-thesis"],"acf":[],"_links":{"self":[{"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/posts\/5859","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/comments?post=5859"}],"version-history":[{"count":3,"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/posts\/5859\/revisions"}],"predecessor-version":[{"id":6096,"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/posts\/5859\/revisions\/6096"}],"wp:attachment":[{"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/media?parent=5859"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/categories?post=5859"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/tags?post=5859"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}