Browsing by Author "Saengja T."

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    SICAR at RRG2024: GPU Poor’s Guide to Radiology Report Generation
    (2024-01-01) Udomlapsakul K.; Pengpun P.; Saengja T.; Veerakanjana K.; Tiankanon K.; Khlaisamniang P.; Supholkhan P.; Chinkamol A.; Aussavavirojekul P.; Phimsiri H.; Sripo T.; Boonnag C.; Tongdee T.; Siriapisith T.; Saiviroonporn P.; Kinchagawat J.; Ittichaiwong P.; Udomlapsakul K.; Mahidol University
    Radiology report generation (RRG) aims to create free-text radiology reports from clinical imaging. Our solution employs a lightweight multimodal language model (MLLM) enhanced with a two-stage post-processing strategy, utilizing a Large Language Model (LLM) to boost diagnostic accuracy and ensure patient safety. We introduce the "First, Do No Harm" SafetyNet, which incorporates X-Raydar, an advanced X-ray classification model, to cross-verify the model outputs and specifically address false negative errors from the MLLM. This comprehensive approach combines the efficiency of lightweight models with the robustness of thorough post-processing techniques, offering a reliable solution for radiology report generation. Our system achieved fourth place on the F1-Radgraph metric for findings generation in the Radiology Report Generation Shared Task (RRG24).1,..