The authors introduce a 3D CT-based visual question answering benchmark with six error types and three task levels, and show that current 3D medical MLLMs perform poorly on it.
A dataset of clinically generated visual questions and answers about radiology images.Scientific data, 5(1):1–10, 2018
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MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports
The authors introduce a 3D CT-based visual question answering benchmark with six error types and three task levels, and show that current 3D medical MLLMs perform poorly on it.