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

Abstract: 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).