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CVPR26-3DCTFMCompetition Dataset
This repository contains data prepared for the CVPR 2026 Workshop Challenge: Foundation Models for General CT Image Diagnosis.
Data origin
The datasets included here are from previously published works:
COVID-CT
- Rahimzadeh, M., Attar, A., & Sakhaei, S. M. (2021). A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset. Biomedical Signal Processing and Control, 102588. https://doi.org/10.1016/j.bspc.2021.102588
LUNA25
- Peeters, D., Obreja, B., Antonissen, N., & Jacobs, C. (2025). Benchmarking of Artificial Intelligence and Radiologists for Lung Cancer Screening in CT: The LUNA25 Challenge. Medical Image Computing and Computer Assisted Intervention 2025 (MICCAI). Zenodo. https://zenodo.org/records/14223624; https://zenodo.org/records/14673658
AMOS
- Ji, Y., Bai, H., Ge, C., Yang, J., Zhu, Y., Zhang, R., Li, Z., Zhang, L., Ma, W., Wan, X., et al. (2022). AMOS: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation. Advances in Neural Information Processing Systems, 35, 36722–36732.
Modifications
The data released in this repository are from the original sources and were prepared for challenge use. This may include file format standardization, and train/validation split preparation.
This repository does not claim ownership of the original datasets. Users should refer to the original sources for full dataset descriptions, intended use, and any official updates.
License and attribution
The redistributed data remain subject to the license terms of their respective original sources. Users must preserve appropriate attribution to the original creators and sources.
Please consult the original dataset pages and publications for the exact licensing terms that apply to each component dataset before reuse or redistribution.