Pith. sign in

REVIEW 5 cited by

Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2303.05977 v2 pith:AA22BAMS submitted 2023-03-10 cs.CV

Tom van Sonsbeek , Mohammad Mahdi Derakhshani , Ivona Najdenkoska , Cees G. M. Snoek , Marcel Worring This is my paper · ORCID
classification cs.CV
keywords languagemedicalmodelsapproachquestionvisualansweringexisting
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Medical Visual Question Answering (VQA) is an important challenge, as it would lead to faster and more accurate diagnoses and treatment decisions. Most existing methods approach it as a multi-class classification problem, which restricts the outcome to a predefined closed-set of curated answers. We focus on open-ended VQA and motivated by the recent advances in language models consider it as a generative task. Leveraging pre-trained language models, we introduce a novel method particularly suited for small, domain-specific, medical datasets. To properly communicate the medical images to the language model, we develop a network that maps the extracted visual features to a set of learnable tokens. Then, alongside the question, these learnable tokens directly prompt the language model. We explore recent parameter-efficient fine-tuning strategies for language models, which allow for resource- and data-efficient fine-tuning. We evaluate our approach on the prime medical VQA benchmarks, namely, Slake, OVQA and PathVQA. The results demonstrate that our approach outperforms existing methods across various training settings while also being computationally efficient.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. HSCR: Hierarchical Self-Contrastive Rewarding for Aligning Medical Vision Language Models

    cs.CV 2025-06 conditional novelty 6.0 of 10

    HSCR uses visual token dropout and logit contrast to construct self-generated dispreferred answers, then trains a medical VLM with explicit and implicit preference losses, improving zero-shot Rad-VQA, SLAKE, and PathV...

  2. Knowing or Guessing? Robust Medical Visual Question Answering via Joint Consistency and Contrastive Learning

    cs.CL 2025-08 conditional novelty 5.0 of 10

    RoMed and CCL: a 144k-question perturbation benchmark for medical VQA and a consistency-plus-contrastive training method that improves LLaVA-Med's accuracy and reduces answer variation.

  3. MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A medical reasoning segmentation model built from a multimodal LLM and a SAM-style mask decoder, trained on a newly generated 10,000-pair medical QA-mask dataset.

  4. Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models

    cs.CV 2025-07 reject novelty 4.0 of 10

    Expert-CFG combines entropy-based uncertainty selection with classifier-free guidance over expert-highlighted text to refine MedVLM outputs, reporting gains on VQA-RAD, SLAKE, and PathVQA.

  5. Adapting Lightweight Vision Language Models for Radiological Visual Question Answering

    cs.CV 2025-06 reject novelty 4.0 of 10

    A 3B PaliGemma model fine-tuned with synthetic QA pairs and two-stage training reaches 41.5% accuracy on open-ended radiology VQA, about 15 points below LLaVA-Med.

Pith tools