RadImageNet-VQA: A Large-Scale CT and MRI Dataset for Radiologic Visual Question Answering

1. 1Collect and curate a large-scale dataset of CT and MRI images
2. 2Annotate the images with expert-curated question-answer samples
3. 3Develop and train models for radiologic visual question answering using the dataset
4. 4Evaluate and fine-tune the models for improved performance

AI engineers and researchers in the medical imaging field can benefit from this dataset to develop and fine-tune models for radiologic visual question answering, while data scientists and analysts can utilize it to improve medical diagnosis accuracy

💡 RadImageNet-VQA provides a large-scale dataset for advancing radiologic visual question answering on CT and MRI exams, addressing the limitations of existing medical VQA datasets

📚 Introducing RadImageNet-VQA, a large-scale dataset for radiologic visual question answering on CT and MRI exams! 💡