为了拓展OmniMedVQA数据集的多样性,研究人员通过GPT-4对QA模版进行复写。同时,为了便于评测,让GPT-4为每个条目配置错误答案,将其构造成选择题的形式,通过这种方式,在确保语义不变的前提下,使不同VQA条目的问答形式更多样。该数据集旨在为医学多模态大模型的发展提供评测基准。详情请参见五号雷达:5radar.com/dataset...
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....
该模型能够执行包括医疗报告生成、视觉问题回答(VQA)和医学图像中的疾病识别等任务。其对图像和文本临床数据的综合处理显著提高了诊断的准确性。 作者的实证评估证实了 MiniGPT-Med 在疾病定位、医疗报告生成和VQA基准测试中的优越性能,这代表着在缩小辅助放射学实践差距方面迈出了重要的一步。 此外,它在医疗报告生成...
Code:https://github.com/abachaa/VQA-Med-2021/tree/main/EvaluationCode Reference If you use the VQA-Med 2021 dataset, please cite our paper: "Overview of the VQA-Med Task at ImageCLEF 2021: Visual Question Answering and Generation in the Medical Domain". Asma Ben Abacha, Mourad Sarrouti,...
As this model was not designed to do few-shot learning (e.g. the image information is prepended to the overall input), we report two modes for MedVINT: zero-shot and fine-tuned, where the model was fine-tuned on the training split of the VQA dataset. Since the rather small Visual-...
It stands out as the largest real-world medical report VQA dataset, offering high-quality OCR results and detailed annotations. The dataset is designed to improve Large Multi-modal Models (LMMs) by enabling them to accurately interpret medical report across a wide range of layouts and to perform...
Evaluated on the VQA-Med 2019 dataset, the proposed model achieved an overall classification accuracy of 0.639. The experimental results demonstrated that the proposed method has superior performance compared to existing methods on the VQA-Med 2019 dataset....
due to existing medical VQA datasets being narrowly focused on image interpretation among the specialties of radiology and pathology, we create Visual USMLE, a challenging generative VQA dataset of complex USMLE-style problems across specialties, which are augmented with images, case vignettes, and pote...
作者的实证评估证实了 MiniGPT-Med 在疾病定位、医疗报告生成和VQA基准测试中的优越性能,这代表着在缩小辅助放射学实践差距方面迈出了重要的一步。 此外,它在医疗报告生成上的表现达到了最先进水平,比之前最佳模型的准确率高出 19%。MiniGPT-Med 有望成为放射学诊断的通用接口,提高广泛医疗成像应用中的诊断效率。
请问如何评估LLaVA1.5在TextVQA test dataset上的结果 2024-10-18· 湖南 回复喜欢 AI 小学生 请教博主一个问题:目前了解到的训练范式有 3 种,只做指令微调(finetuning)、两阶段训练(pretraining+finetuning)、vit 训练+两阶段微调(Yi-VL 是这种)。如果我想让训练后的 llava 模型新认识某个人/名字(...