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SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering

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arxiv 2102.09542 v1 pith:QOOIELZ7 submitted 2021-02-18 cs.CV cs.AIcs.CL

Bo Liu , Li-Ming Zhan , Li Xu , Lin Ma , Yan Yang , Xiao-Ming Wu This is my paper · ORCID
classification cs.CVcs.AIcs.CL
keywords slakedatasetmed-vqamedicalansweringdevelopmentevaluationquestion
verification ladder T0 review T1 audit T2 compute T3 formal
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Medical visual question answering (Med-VQA) has tremendous potential in healthcare. However, the development of this technology is hindered by the lacking of publicly-available and high-quality labeled datasets for training and evaluation. In this paper, we present a large bilingual dataset, SLAKE, with comprehensive semantic labels annotated by experienced physicians and a new structural medical knowledge base for Med-VQA. Besides, SLAKE includes richer modalities and covers more human body parts than the currently available dataset. We show that SLAKE can be used to facilitate the development and evaluation of Med-VQA systems. The dataset can be downloaded from http://www.med-vqa.com/slake.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 10 citations worldwide. Full citation record

  1. MedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical Consultation

    cs.AI 2026-07 conditional novelty 6.5 of 10

    On a new benchmark of 5,620 real multimodal online consultations, top LLMs trail the original physicians mainly because they trigger more unsafe or unsupported negative criteria.

  2. Ming-Omni: A Unified Multimodal Model for Perception and Generation

    cs.AI 2025-06 conditional novelty 6.0 of 10

    A single model with modality-specific routing processes image, text, audio, and video inputs and generates text, speech, and images, with public benchmarks reported across all of these abilities.

  3. RARL: Improving Medical VLM Reasoning and Generalization with Reinforcement Learning and LoRA under Data and Hardware Constraints

    cs.CV 2025-06 reject novelty 5.0 of 10

    RARL fine-tunes Qwen2-VL-2B on 716 medical samples with GRPO, LoRA, and a vaguely defined reasoning reward, claiming gains of 7.78% over SFT on reasoning and up to 27% on unseen VQA benchmarks.

  4. Inference-Time Agentic Decision Rules Beat Longer Evolving Search for Multi-Image Medical Reasoning

    cs.CV 2026-07 conditional novelty 4.0 of 10

    On MedFrameQA, order-vote (57.89%) beats fixed prompting (52.73%) and order-rerank (55.79%), and a single 100-generation run drops final-test accuracy to 56.02%.

  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.

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