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

• URL: http://arxiv.org/abs/2509.04471v2
• Date: Thu, 02 Oct 2025 08:59:44 GMT
• Title: MOSAIC: A Multilingual, Taxonomy-Agnostic, and Computationally Efficient Approach for Radiological Report Classification
• Authors: Alice Schiavone, Marco Fraccaro, Lea Marie Pehrson, Silvia Ingala, Rasmus Bonnevie, Michael Bachmann Nielsen, Vincent Beliveau, Melanie Ganz, Desmond Elliott,
• Abstract summary: We introduce MOSAIC, a multilingual, taxonomy-agnostic, and computationally efficient approach for radiological report classification.<n>Built on a compact open-access language model (MedGemma-4B), MOSAIC supports both zero- prompting/few-shot finetuning and lightweight finetuning.<n>We evaluate MOSAIC across seven datasets in English, Spanish, French, and Danish, spanning multiple imaging modalities and label modalities.
• Score: 8.266250751994187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, compared to 86 with the full 1600-sample training set. MOSAIC offers a practical alternative to large or proprietary LLMs in clinical settings. Code and models are open-source. We invite the community to evaluate and extend MOSAIC on new languages, taxonomies, and modalities.

Related papers

• Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation [23.003923723432436]
  This study systematically evaluated the impact of quantization on 12 state-of-the-art large language models.<n>We show that quantization substantially reduces GPU memory requirements-by up to 75%-while preserving model performance across diverse tasks.
  arXiv  Detail & Related papers  (2025-09-04T04:18:45Z)
• Towards Open Foundation Language Model and Corpus for Macedonian: A Low-Resource Language [4.276396344868335]
  We create resources to facilitate the adoption of Large Language Models (LLMs)<n>We collect the largest Macedonian corpus to date, consisting of 40GB of textual data and totaling 3.5B words.<n>We train domestic-yak, a state-of-the-art 8B- parameter model, on our curated datasets and evaluate it against eight baseline models.
  arXiv  Detail & Related papers  (2025-06-11T09:46:58Z)
• Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval [49.1574468325115]
  We introduce Amharic-specific dense retrieval models based on pre-trained Amharic BERT and RoBERTa backbones.<n>Our proposed RoBERTa-Base-Amharic-Embed model (110M parameters) achieves a 17.6% relative improvement in MRR@10.<n>More compact variants, such as RoBERTa-Medium-Amharic-Embed (42M) remain competitive while being over 13x smaller.
  arXiv  Detail & Related papers  (2025-05-25T23:06:20Z)
• InkubaLM: A small language model for low-resource African languages [9.426968756845389]
  InkubaLM is a small language model with 0.4 billion parameters. It achieves performance comparable to models with significantly larger parameter counts. It demonstrates remarkable consistency across multiple languages.
  arXiv  Detail & Related papers  (2024-08-30T05:42:31Z)
• Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation [113.5002649181103]
  Training open-source small multimodal models (SMMs) to bridge competency gaps for unmet clinical needs in radiology. For training, we assemble a large dataset of over 697 thousand radiology image-text pairs. For evaluation, we propose CheXprompt, a GPT-4-based metric for factuality evaluation, and demonstrate its parity with expert evaluation. The inference of LlaVA-Rad is fast and can be performed on a single V100 GPU in private settings, offering a promising state-of-the-art tool for real-world clinical applications.
  arXiv  Detail & Related papers  (2024-03-12T18:12:02Z)
• ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot Multilingual Information Retrieval [10.664434993386523]
  Current approaches circumvent the lack of high-quality labeled data in non-English languages. We present a novel modular dense retrieval model that learns from the rich data of a single high-resource language.
  arXiv  Detail & Related papers  (2024-02-23T02:21:24Z)
• DeMuX: Data-efficient Multilingual Learning [57.37123046817781]
  DEMUX is a framework that prescribes exact data-points to label from vast amounts of unlabelled multilingual data. Our end-to-end framework is language-agnostic, accounts for model representations, and supports multilingual target configurations.
  arXiv  Detail & Related papers  (2023-11-10T20:09:08Z)
• Extrapolating Multilingual Understanding Models as Multilingual Generators [82.1355802012414]
  This paper explores methods to empower multilingual understanding models the generation abilities to get a unified model. We propose a textbfSemantic-textbfGuided textbfAlignment-then-Denoising (SGA) approach to adapt an encoder to a multilingual generator with a small number of new parameters.
  arXiv  Detail & Related papers  (2023-05-22T15:33:21Z)
• Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts [56.72488923420374]
  Pre-trained language models (LMs) have shown great potential for cross-lingual transfer in low-resource settings. We show the few-shot cross-lingual transfer property of LMs for named recognition (NER) and apply it to solve a low-resource and real-world challenge of code-mixed (Spanish-Catalan) clinical notes de-identification in the stroke.
  arXiv  Detail & Related papers  (2022-04-10T21:46:52Z)
• XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning [68.57658225995966]
  Cross-lingual Choice of Plausible Alternatives (XCOPA) is a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages. We evaluate a range of state-of-the-art models on this novel dataset, revealing that the performance of current methods falls short compared to translation-based transfer.
  arXiv  Detail & Related papers  (2020-05-01T12:22:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.