| { | |
| "channel_names": { | |
| "0": "CT" | |
| }, | |
| "labels": { | |
| "background": 0, | |
| "nodule": 1 | |
| }, | |
| "classification_labels": { | |
| "malignancy": { | |
| "0": "Benign", | |
| "1": "Malignant" | |
| } | |
| }, | |
| "numTraining": 6141, | |
| "file_ending": ".nii.gz", | |
| "name": "Dataset009_LUNA25", | |
| "description": "LUNA25 — cropped low-dose chest CT patches for lung nodule segmentation and malignancy risk estimation (6141 nodule crops from lung cancer screening CTs)", | |
| "reference": "Peeters D, Obreja B, Antonissen N, Jacobs C. 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. 2025. https://doi.org/10.5281/zenodo.15094631", | |
| "licence": "CC BY 4.0", | |
| "release": "1.0" | |
| } | |