Pitikorn Khlaisamniang


2024

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SICAR at RRG2024: GPU Poor’s Guide to Radiology Report Generation
Kiartnarin Udomlapsakul | Parinthapat Pengpun | Tossaporn Saengja | Kanyakorn Veerakanjana | Krittamate Tiankanon | Pitikorn Khlaisamniang | Pasit Supholkhan | Amrest Chinkamol | Pubordee Aussavavirojekul | Hirunkul Phimsiri | Tara Sripo | Chiraphat Boonnag | Trongtum Tongdee | Thanongchai Siriapisith | Pairash Saiviroonporn | Jiramet Kinchagawat | Piyalitt Ittichaiwong
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing

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 Xraydar, an advanced X-ray classification model, to cross-verify the model outputs and specifically address false negatives 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).