i H ealth- C hile-3&2 at RRG 24: Template Based Report Generation (2024)

Abstract: This paper presents the approaches of the iHealth-Chile-3 and iHealth-Chile-2 teams for the shared task of Large-Scale Radiology Report Generation at the BioNLP workshop. Inspired by prior work on template-based report generation, both teams focused on exploring various template-based strategies, using predictions from multi-label image classifiers as input. Our best approach achieved a modest F1-RadGraph score of 19.42 on the findings hidden test set, ranking 7th on the leaderboard. Notably, we consistently observed a discrepancy between our classification metrics and the F1-CheXbert metric reported on the leaderboard, which always showed lower scores. This suggests that the F1-CheXbert metric may be missing some of the labels mentioned by the templates.

## introduction the generation of radiology reports (rrg) from medical imaging using deep learning represents a significant area of ongoing research @xcite . successfully implementing this task can help reduce the workload and time spent on administrative duties, such as composing text reports. this efficiency enables physicians to focus more on patient interaction @xcite and in identifying anomalies from multiple input images. there is a pressing need for explainable ai (xai) @xcite in critical domains like medicine. in the context of report generation, the explainability aspect remains understudied @xcite . some models address this issue by generating saliency maps that highlight important pixels, using techniques such as grad-cam @xcite for cnn networks or visualizing attention maps for transformer networks. however, some authors argue against relying solely on saliency maps as explanations. for instance, @xcite advocates for using inherently interpretable models that are constrained by domain knowledge, making them transparent and understandable for humans. to enhance transparency and understandability of our implementation in the shared task @xcite , we use a simple template-based report generation model. specifically, we reimplement and modify the template-based strategy proposed by @xcite . the team ihealth-chile-3 focused on meticulously reproducing pino et al.'s approach, employing densenet-121 and a conventional multilabel classification layer for 13 chexpert classes (excluding "no findings"), as shown in figure 2 . meanwhile, team ihealth-chile-2 developed a different image classifier that combines densenet-121 with text embeddings of factual statements, which can be both classified and visually grounded, leveraging very recent work on fact extraction and encoding from radiology reports @xcite . this approach, shown in figure 3 , can be seen as a more general version of stage 1 of chexfusion @xcite , the winning method in the iccv cvamd 2023 shared task on cxr-lt: multi-label long-tailed classification on chest x-rays @xcite . ## conclusions and future work we have presented the results of the ihealth-chile-3 and ihealth-chile-2 teams in the large-scale radiology report generation shared task. both teams used a template-based method, where an image classifier predicts specific classes, which are then used to generate a report with predefined templates. the performance in the challenge was modest. interestingly, despite the templates being tailored for chexpert classes, the f1-chexbert metrics were consistently lower than the classification metrics. based on these results, future work should focus on: (1) thoroughly evaluating report generation metrics to identify and address limitations in existing ones; (2) improving chest x-ray image classifiers, particularly for long-tail classes; and (3) developing more advanced report generation systems that surpass rigid templates while preserving classifier accuracy for long-tail classes.