Add task category and link to paper
#2
by
nielsr
HF Staff
- opened
README.md
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license: apache-2.0
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---
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# 3D-RAD
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The official Dataset for the paper "3D-RAD: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Tasks".
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In our project, we collect a large-scale dataset designed to advance 3D Med-VQA using radiology CT scans, 3D-RAD, encompasses six diverse VQA tasks: anomaly detection (task 1), image observation (task 2), medical computation (task 3), existence detection (task 4), static temporal diagnosis (task 5), and longitudinal temporal diagnosis (task 6).
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| [RadFM](https://github.com/chaoyi-wu/RadFM) | Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data | https://github.com/chaoyi-wu/RadFM |
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| [M3D](https://github.com/BAAI-DCAI/M3D) | M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models |
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| OmniV(not open) | OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding |
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license: apache-2.0
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task_categories:
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- image-text-to-text
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# 3D-RAD
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The official Dataset for the paper "[3D-RAD: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Tasks](https://huggingface.co/papers/2506.11147)".
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In our project, we collect a large-scale dataset designed to advance 3D Med-VQA using radiology CT scans, 3D-RAD, encompasses six diverse VQA tasks: anomaly detection (task 1), image observation (task 2), medical computation (task 3), existence detection (task 4), static temporal diagnosis (task 5), and longitudinal temporal diagnosis (task 6).
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| ----- | ------------------------------------------------------------ |
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| [RadFM](https://github.com/chaoyi-wu/RadFM) | Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data | https://github.com/chaoyi-wu/RadFM |
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| [M3D](https://github.com/BAAI-DCAI/M3D) | M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models |
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| OmniV(not open) | OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding |
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