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- W4367301040 abstract "Compared with the general Visual Question Answering (VQA), Medical VQA is more challenging. Medical images contain more complex information than general images. Aiming at this point, we propose the IIF module that can improve the model's ability to obtain visual feature. In addition, we design QAM to help the model analyze the question better. On the VQA-RAD dataset, the accuracy of our model improved to 66.4% on the opened-ended questions and 80.1% on the closed-ended questions, outperforming other relevant models. The results on the VQA-MED 2019 dataset also verify the effectiveness of our model." @default.
- W4367301040 created "2023-04-29" @default.
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- W4367301040 date "2023-04-29" @default.
- W4367301040 modified "2023-10-16" @default.
- W4367301040 title "A multi-modal model based on transformers for medical visual question answering" @default.
- W4367301040 doi "https://doi.org/10.1117/12.2671434" @default.
- W4367301040 hasPublicationYear "2023" @default.
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