mlsanityData to 7 Oct 2026

RE-tune: Incremental Fine Tuning of Biomedical Vision-Language Models for Multi-label Chest X-ray Classification

arXiv 2410.17827 ↗First posted 23 Oct 2024cs.AIcited by 2

On the map: Multimodal LLMs / vision-language models → Medical vision-language models

Abstract (arXiv)

In this paper we introduce RE-tune, a novel approach for fine-tuning pre-trained Multimodal Biomedical Vision-Language models (VLMs) in Incremental Learning scenarios for multi-label chest disease diagnosis. RE-tune freezes the backbones and only trains simple adaptors on top of the Image and Text encoders of the VLM. By engineering positive and negative text prompts for diseases, we leverage the ability of Large Language Models to steer the training trajectory. We evaluate RE-tune in three realistic incremental learning scenarios: class-incremental, label-incremental, and data-incremental. Our results demonstrate that Biomedical VLMs are natural continual learners and prevent catastrophic forgetting. RE-tune not only achieves accurate multi-label classification results, but also prioritizes patient privacy and it distinguishes itself through exceptional computational efficiency, rendering it highly suitable for broad adoption in real-world healthcare settings.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 8 of its 10 references that are in the map. First posted on arXiv 23 Oct 2024.

Builds on

8 of 10 references.

References · 8

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  4. 2020Contrastive Learning of Medical Visual Representations from Paired Images and Textcited by 636
  5. 2019MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographscited by 590
  6. 2020Class-incremental learning: survey and performance evaluation on image classificationcited by 446
  7. 2022Making the Most of Text Semantics to Improve Biomedical Vision--Language Processingcited by 336
  8. 2020CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERTcited by 329

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025When Continue Learning Meets Multimodal Large Language Model: A Surveycited by 8
  2. 2025Position: Restructuring of Categories and Implementation of Guidelines Essential for VLM Adoption in Healthcarecited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Med-AR: Autoregressive Vision-Language Pretraining for Long-Tailed Chest X-Ray Classification and Uncertainty-Aware Evaluation
  2. 2025Adapting Foundation Vision-Language Models to Medical Diagnosis via Query-Driven Expert Bridging
  3. 2025Scaling Down to Scale Up: Towards Operationally-Efficient and Deployable Clinical Models via Cross-Modal Low-Rank Adaptation for Medical Vision-Language Models
  4. 2025Eyes on the Image: Gaze Supervised Multimodal Learning for Chest X-ray Diagnosis and Report Generation
  5. 2025A Generative Framework for Bidirectional Image-Report Understanding in Chest Radiography
  6. 2025Medical Knowledge Intervention Prompt Tuning for Medical Image Classification
  7. 2026CADRE: Stable, Parameter Efficient Adaptation of Medical Vision Language Models with Bounded Forgetting and Prior Drift
  8. 2026Measuring and Aligning Abstraction in Vision-Language Models with Medical Taxonomies