GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generation
Medical imaging is crucial for diagnosing, monitoring, and treating medical conditions. The medical reports of radiology images are the primary medium through which medical professionals can attest to their findings, but their writing is time-consuming and requires specialized clinical expertise. Th...
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MDPI AG
2025-06-01
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| author | Iustin Sîrbu Iulia-Renata Sîrbu Jasmina Bogojeska Traian Rebedea |
| author_facet | Iustin Sîrbu Iulia-Renata Sîrbu Jasmina Bogojeska Traian Rebedea |
| author_sort | Iustin Sîrbu |
| collection | DOAJ |
| description | Medical imaging is crucial for diagnosing, monitoring, and treating medical conditions. The medical reports of radiology images are the primary medium through which medical professionals can attest to their findings, but their writing is time-consuming and requires specialized clinical expertise. Therefore, the automated generation of radiography reports has the potential to improve and standardize patient care and significantly reduce the workload of clinicians. Through our work, we have designed and evaluated an end-to-end transformer-based method to generate accurate and factually complete radiology reports for X-ray images. Additionally, we are the first to introduce curriculum learning for end-to-end transformers in medical imaging and demonstrate its impact in obtaining improved performance. The experiments were conducted using the MIMIC-CXR-JPG database, the largest available chest X-ray dataset. The results obtained are comparable with the current state of the art on the natural language generation (NLG) metrics BLEU and ROUGE-L, while setting new state-of-the-art results on F1 examples-averaged F1-macro and F1-micro metrics for clinical accuracy and on the METEOR metric widely used for NLG. |
| format | Article |
| id | doaj-art-6dcede9afabd4f9fa079a59653ce9678 |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-6dcede9afabd4f9fa079a59653ce96782025-08-20T02:45:42ZengMDPI AGInformation2078-24892025-06-0116752410.3390/info16070524GIT-CXR: End-to-End Transformer for Chest X-Ray Report GenerationIustin Sîrbu0Iulia-Renata Sîrbu1Jasmina Bogojeska2Traian Rebedea3Faculty of Automatic Control and Computer Science, National University of Science and Technology Politehnica of Bucharest, 060042 Bucharest, RomaniaFaculty of Automatic Control and Computer Science, National University of Science and Technology Politehnica of Bucharest, 060042 Bucharest, RomaniaSchool of Engineering, Zurich University of Applied Sciences, 8401 Winterthur, SwitzerlandFaculty of Automatic Control and Computer Science, National University of Science and Technology Politehnica of Bucharest, 060042 Bucharest, RomaniaMedical imaging is crucial for diagnosing, monitoring, and treating medical conditions. The medical reports of radiology images are the primary medium through which medical professionals can attest to their findings, but their writing is time-consuming and requires specialized clinical expertise. Therefore, the automated generation of radiography reports has the potential to improve and standardize patient care and significantly reduce the workload of clinicians. Through our work, we have designed and evaluated an end-to-end transformer-based method to generate accurate and factually complete radiology reports for X-ray images. Additionally, we are the first to introduce curriculum learning for end-to-end transformers in medical imaging and demonstrate its impact in obtaining improved performance. The experiments were conducted using the MIMIC-CXR-JPG database, the largest available chest X-ray dataset. The results obtained are comparable with the current state of the art on the natural language generation (NLG) metrics BLEU and ROUGE-L, while setting new state-of-the-art results on F1 examples-averaged F1-macro and F1-micro metrics for clinical accuracy and on the METEOR metric widely used for NLG.https://www.mdpi.com/2078-2489/16/7/524radiology report generationcurriculum learningimage captioningchest X-raytransformermachine learning |
| spellingShingle | Iustin Sîrbu Iulia-Renata Sîrbu Jasmina Bogojeska Traian Rebedea GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generation Information radiology report generation curriculum learning image captioning chest X-ray transformer machine learning |
| title | GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generation |
| title_full | GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generation |
| title_fullStr | GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generation |
| title_full_unstemmed | GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generation |
| title_short | GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generation |
| title_sort | git cxr end to end transformer for chest x ray report generation |
| topic | radiology report generation curriculum learning image captioning chest X-ray transformer machine learning |
| url | https://www.mdpi.com/2078-2489/16/7/524 |
| work_keys_str_mv | AT iustinsirbu gitcxrendtoendtransformerforchestxrayreportgeneration AT iuliarenatasirbu gitcxrendtoendtransformerforchestxrayreportgeneration AT jasminabogojeska gitcxrendtoendtransformerforchestxrayreportgeneration AT traianrebedea gitcxrendtoendtransformerforchestxrayreportgeneration |