A naukri.com initiative
Arxiv
3d
35
Image Credit: Arxiv
Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal Endoscopy
- Kvasir-VQA-x1 is a new multimodal dataset designed for medical reasoning and robust MedVQA in gastrointestinal endoscopy.
- The dataset addresses the limitations of current datasets by incorporating 159,549 new question-answer pairs to test deeper clinical reasoning.
- Questions in the dataset are stratified by complexity to evaluate a model's inference capabilities more effectively.
- To prepare models for real-world clinical scenarios, visual augmentations that simulate common imaging artifacts have been included in the dataset.
- Kvasir-VQA-x1 supports two evaluation tracks: one for standard VQA performance and the other to assess model robustness against visual perturbations.
- The dataset aims to accelerate the development of more reliable and effective AI systems for clinical use by providing a challenging and clinically relevant benchmark.
- Kvasir-VQA-x1 adheres to FAIR data principles, ensuring accessibility and transparency for the wider research community.
- Code and data related to the dataset can be found on GitHub at https://github.com/Simula/Kvasir-VQA-x1
- Access to the dataset is available at https://huggingface.co/datasets/SimulaMet/Kvasir-VQA-x1
Read Full Article
2 Likes
For uninterrupted reading, download the app