著名的数据集包括 VQA-MED(Hasan 等人,2018b)、Abacha 等人(2019,2020)、VQA-RAD(Lau 等人,2018b)和 PathVQA(He 等人,2020a)。 随着2018 年 VQA-Med 挑战的推出,医学 VQA 的研究加速,许多方法受到通用领域模型的启发。常用的注意力模...
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...
I used the VQA-Med 2019 and VQA-Med 2020 training datasets to train my models. In the VQG task, I presented a variational autoencoders model that takes as input an image and generates a question as output. I also generated new training data from the existing VQA-Med 2020 VQG dataset,...
原文地址:https://dl.acm.org/doi/10.1145/3394171.3413761 代码地址:https://github.com/awenbocc/med-vqa 这篇文章是香港理工大学在ACM Multimedia 2020上发表的工作。基于前文[4]利用元学习在数据和图像处理阶段做出的改进,提出QCR(question-conditional reasoning)和TCR(type-conditional reasoning)模块进一步获取问题...
第一,它使用了一个多任务模型(MED),将多种任务预训练整合在了一起。 从框架图中看到,MED主要包括3个部分: 单峰编码器,可以用图像-文本对比损失(ITC)训练,让视觉和文本表征对齐。 基于图像的文本编码器,可以用传统的交叉注意层来模拟视觉-语言信息的转换,并通过图像-文本匹配损失(ITM)来进行训练,从而来区分正、...
为了训练一个统一的多模态模型,作者提出了多模态混合编码-解码器(MED),这是一个多任务模型,可以在以下三个功能中的一个运行: 单模态编码器(Unimodal encoder):单模态编码器,分别编码图像和文本。文本编码器与BERT相同,其中[CLS]标记被添加在文本输入开头用来总结句子。 基于图像的文本编码器(Image-grounded text en...
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...
Medical Visual Question Answering (MedVQA) aims to develop models to answer clinically relevant questions on medical images. A major challenge in developing VQA for the Medical domain is the unavailability of large, well-annotated MedVQA datasets. Using
The performance of MedVQA systems depends mainly on the method used to combine the features of the two input modalities: medical image and question. In this work, we propose an architecturally simple fusion strategy that uses multi-head self-attention to combine medical images and questions of ...
PathVQA He et al. (2020)PathologyPEIR Digital Library Jones et al. (2001)5k32.8k SLAKE Liu et al. (2021b)RadiologyMSD Antonelli et al. (2022), ChestX-ray8 Wang et al. (2017), CHAOS Kavur et al. (2021)0.7k14k VQA-Med-2021 Ben Abacha et al. (2021)RadiologyMedPix® database...