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README.md ADDED
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+ ---
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+ tags:
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+ - medical-imaging
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+ - segmentation
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+ - nnu-net
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+ - airway
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+ - ct
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+ ---
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+
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+ # CaliTree weights
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+
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+ These are the trained weights for CaliTree, a solution to the ATM'26 (MICCAI 2026) airway tree modelling challenge.
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+
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+ Code: **[adinathdukre/CaliTree](https://github.com/adinathdukre/CaliTree)**
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+
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+ | folder | model | use |
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+ |---|---|---|
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+ | `track1/nnUNet_ckpts` | nnU-Net ResEnc-M, `nnUNetTrainerAirwayCB_clprec025`, 5 folds | Track 1 binary airway segmentation |
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+ | `track2/nnUNet_ckpts` | nnU-Net ResEnc-M, `nnUNetTrainerAirwayCB`, 5 folds | Track 2 stage 1 airway mask |
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+ | `track2/nnUNet_t2warm` | nnU-Net `3d_fullres_SAS`, `nnUNetTrainerT2Warm`, 5 folds | Track 2 stage 2 21-class voxel labelling |
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+
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+ Each track folder is used directly as `ATM_RESOURCES`:
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+
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+ ```bash
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+ huggingface-cli download adidukre/CaliTree --local-dir /path/to/weights
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+ ATM_RESOURCES=/path/to/weights/track1 ATM_INPUT=/path/to/ct_folder ATM_OUTPUT=/path/to/output \
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+ python docker/T1/inference.py
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+ ```
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+ "numTraining": 299,
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+ "file_ending": ".mha",
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+ "description": "ATM'26 Track-1 airway segmentation. Labels encode calibre band (1/2/3 = large/mid/distal, R1=2.5mm R2=1.0mm, assigned by nearest centerline point) plus +3 inside the centerline tube (skeleton dilated twice).",
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