mlsanityData to 7 Oct 2026

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

arXiv 2102.09542 ↗First posted 18 Feb 2021cs.CV cs.AI cs.CLcited by 444

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Cited by 233 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 444 papers in the map (data through 7 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 18 Feb 2021.

Builds on

9 of 15 references.

References · 9

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2015VQA: Visual Question Answeringcited by 2,489
  3. 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,445
  4. 2019A large annotated medical image dataset for the development and evaluation of segmentation algorithmscited by 371
  5. 2015Stacked Attention Networks for Image Question Answeringcited by 273
  6. 2016FVQA: Fact-based Visual Question Answeringcited by 181
  7. 2016Analyzing the Behavior of Visual Question Answering Modelscited by 119
  8. 2015Yin and Yang: Balancing and Answering Binary Visual Questionscited by 109
  9. 2019Overcoming Data Limitation in Medical Visual Question Answeringcited by 71

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 5; 2023: 35; 2024: 95; 2025: 137; 2026: 170.

444 papers.

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  1. 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Daycited by 1,287
  2. 2023A Survey on Multimodal Large Language Modelscited by 898
  3. 2023Instruction Tuning for Large Language Models: A Surveycited by 617
  4. 2023BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairscited by 506
  5. 2023PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answeringcited by 333
  6. 2023Towards Generalist Biomedical AIcited by 286
  7. 2025Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoningcited by 217
  8. 2023PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documentscited by 212
  9. 2025MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understandingcited by 212
  10. 2024NVILA: Efficient Frontier Visual Language Modelscited by 206
  11. 2023BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Taskscited by 201
  12. 2024HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scalecited by 194
38 more
  1. 2024OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLMcited by 182
  2. 2023Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Datacited by 176
  3. 2025MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learningcited by 172
  4. 2023A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethicscited by 162
  5. 2026Qwen3.5-Omni Technical Reportcited by 146
  6. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
  7. 2024Vision-Language Models for Medical Report Generation and Visual Question Answering: A Reviewcited by 121
  8. 2025HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptationcited by 112
  9. 2024MMedAgent: Learning to Use Medical Tools with Multi-modal Agentcited by 108
  10. 2023Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuningcited by 106
  11. 2022Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Trainingcited by 104
  12. 2024A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discoverycited by 96
  13. 2021Medical Visual Question Answering: A Surveycited by 93
  14. 2021Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?cited by 85
  15. 2024MoVA: Adapting Mixture of Vision Experts to Multimodal Contextcited by 82
  16. 2024MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicinecited by 79
  17. 2025MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflowcited by 71
  18. 2024FairCLIP: Harnessing Fairness in Vision-Language Learningcited by 69
  19. 2025MedRAX: Medical Reasoning Agent for Chest X-raycited by 68
  20. 2024CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Modelscited by 64
  21. 2025Ming-Omni: A Unified Multimodal Model for Perception and Generationcited by 62
  22. 2023Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Modelscited by 58
  23. 2024Detecting and Evaluating Medical Hallucinations in Large Vision Language Modelscited by 57
  24. 2024ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Datasetcited by 54
  25. 2023EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Imagescited by 53
  26. 2023ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogramcited by 52
  27. 2022Align, Reason and Learn: Enhancing Medical Vision-and-Language Pre-training with Knowledgecited by 51
  28. 2024VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledgecited by 50
  29. 2024MedCoT: Medical Chain of Thought via Hierarchical Expertcited by 46
  30. 2024A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicinecited by 45
  31. 2024Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Modelscited by 43
  32. 2026MedGemma 1.5 Technical Reportcited by 43
  33. 2025Improving Medical Large Vision-Language Models with Abnormal-Aware Feedbackcited by 42
  34. 2023BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Modelscited by 41
  35. 2025MMedAgent-RL: Optimizing Multi-Agent Collaboration for Multimodal Medical Reasoningcited by 37
  36. 2023Towards Unifying Medical Vision-and-Language Pre-training via Soft Promptscited by 35
  37. 2025Visual Large Language Models for Generalized and Specialized Applicationscited by 35
  38. 2025MedEyes: Learning Dynamic Visual Focus for Medical Progressive Diagnosiscited by 32

50 most cited of 444 All 444 (JSON)