Work

MOSAIC: A Multilingual, Taxonomy-Agnostic, and Computationally Efficient Approach for Radiological Report Classification in Low-Resource Settings

Year: 2026

Type: conference-paper

Abstract: Radiology reports contain rich clinical information that can be used to train imaging models without relying on costly manual annotation.However, existing approaches face critical limitations: rule-based methods struggle with linguistic variability, supervised models require large annotated datasets, and recent LLM-based systems depend on closed-source or resource-intensive models that are unsuitable for clinical use.Moreover, current solutions are largely restricted to English and single-modality, single-taxonomy datasets.We introduce MOSAIC, a multilingual, taxonomy-agnostic, and computationally efficient approach for radiological report classification.Built on a compact open-access language model (MedGemma-4B), MOSAIC supports both zero-/few-shot prompting and lightweight fine-tuning, enabling deployment on consumer-grade GPUs.We evaluate MOSAIC across seven datasets in English, Spanish, French, and Danish, spanning multiple imaging modalities and label taxonomies.The model achieves a mean macro F1 score of 88 across five chest X-ray datasets, approaching or exceeding expert-level performance, while requiring only 24 GB of GPU memory.With data augmentation, as few as 80 annotated samples are sufficient to reach a weighted F1 score of 82 on Danish reports, enabling large-scale cohort classification with minimal human effort.Code and models are open-source, offering a practical alternative to large or proprietary LLMs in clinical settings.

Source: Language Resources and Evaluation Conference (LREC)

DOI: 10.63317/3qvne99cm779