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In recent years, artificial intelligence has played an important role inmedicine and disease diagnosis, with many applications to be mentioned, one ofwhich is Medical Visual Question Answering (MedVQA). By combining computervision and natural language processing, MedVQA systems can assist experts inextracting relevant information from medical image based on a given questionand providing precise diagnostic answers. The ImageCLEFmed-MEDVQA-GI-2023challenge carried out visual question answering task in the gastrointestinaldomain, which includes gastroscopy and colonoscopy images. Our team approachedTask 1 of the challenge by proposing a multimodal learning method with imageenhancement to improve the VQA performance on gastrointestinal images. Themultimodal architecture is set up with BERT encoder and different pre-trainedvision models based on convolutional neural network (CNN) and Transformerarchitecture for features extraction from question and endoscopy image. Theresult of this study highlights the dominance of Transformer-based visionmodels over the CNNs and demonstrates the effectiveness of the imageenhancement process, with six out of the eight vision models achieving betterF1-Score. Our best method, which takes advantages of BERT+BEiT fusion and imageenhancement, achieves up to 87.25% accuracy and 91.85% F1-Score on thedevelopment test set, while also producing good result on the private test setwith accuracy of 82.01%.

UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering