VQA-Med-2019 是一个专注于医学领域的视觉问答数据集,旨在通过图像内容分析来解答问题,无须额外的医学专业知识或领域内推理。它包含四个主要问题类别:影像模态(Modality)、成像平面(Plane)、器官系统(Organ System)以及异常情况(Abnormality)。这些问题按不同的难度级别设计,以适应多样的分类和文本生成方法。数据集共含...
我们利用OmniMedVQA数据集,测试了8个通用多模态大模型:BILP2, MiniGPT-4, InstructBLIP, mPLUGOwl, Otter, LLaVA, LLama adapter v2, 和VPGTrans。以及四个医学多模态模型:Med-Flamingo,RadFM,MedVInT和 LLaVA-Med。实验结果如图5和图6所示,它们分别按5种不同任务类型和12种不同模态体现了各模型的评测结果。
Results of the VQA-Med-2021 challenge on crowdAI: VQA task:https://www.aicrowd.com/challenges/imageclef-2021-vqa-med-vqa VQG task:https://www.aicrowd.com/challenges/imageclef-2021-vqa-med-vqg Data: VQA Data: Training set: We provided the VQA-Med 2020 training data including 4,500 radi...
【VQA文献阅读】VQA-Med: Overview of the Medical Visual Question Answering Task at ImageCLEF 2019,程序员大本营,技术文章内容聚合第一站。
ImageCLEF 2020 VQA-Med - VQA 1 Authorship/Co-Authorship By ImageCLEF 13.4k 313 0 91 Share z_liao0.496 TheInceptionTeam0.48 bumjun_jung0.466 Leaderboard filters Δ#ParticipantsAccuracyBleuEntriesLast SubmissionSubmission Trend 01 z_liao0.4960.5425Fri, 5 Jun 2020 19:49...
The ImageCLEF 2018 VQA-Med challenge has officially ended and we would like to thank everyone for their participation. The official results are already emailed to corresponding participants. Post-challenge submissions and the leaderboard will remain enabled for a few weeks so you will still be able...
National Library of Medicine (NLM) in Visual Question Answering (VQA) and Visual Question Generation (VQG) tasks of the VQA-Med challenge at ImageCLEF 2020. In the VQA task, I proposed a variational autoencoders model that takes as input a medical question-image pair and generates a ...
建立OmniMedVQA数据集并全面评估现有模型，发现普遍性能未达预期。未来，应专注于特定器官或疾病，构建专门化医学LVLM，为医学多模态大模型发展提供评测基准。此数据集有望促进医学人工智能水平提升，实现医疗领域技术创新。参考文献：[1] Wenqi Shao, Yutao Hu, Peng Gao, Meng Lei, Kaipeng Zhang, Fan...
PathVQA PMC-VQA Med-Halt We thank the authors for their open-sourced code/data and encourage users to cite their works when applicable. If you use this code or data for your research, please cite our work: @article{wu2024hallucination, title={Hallucination Benchmark in Medical Visual Question...
MedPromptX-VQA Introduced by Shaaban et al. in MedPromptX: Grounded Multimodal Prompting for Chest X-ray Diagnosis A new in-context visual question answering dataset encompassing interleaved image and EHR data derived from MIMIC-IV and MIMIC-CXR-JPG databases....