Furen Xiao
77adc2b3af
Introduce the third segmentation pipeline using MONAI (1.6.x) to allow direct comparison with Pipelines A and B. This includes the implementation of the iterative pseudo-labeling workflow, training scripts, and inference protocols. - Add `scripts_monai/` directory containing the MONAI pipeline scripts. - Update documentation in `README.md` and `AGENTS.md` to include MONAI package requirements and pipeline details. - Configure `.gitignore` to exclude MONAI-specific run directories. - Update data directory descriptions to include MONAI pseudo-labels.
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889 B
Markdown
23 lines
No EOL
889 B
Markdown
# AGENTS.md
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## Environment
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This project uses a conda environment named `longitudinal` (Python 3.14).
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Activate with:
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```bash
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source /opt/conda/etc/profile.d/conda.sh && conda activate longitudinal
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```
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Key packages: torch 2.14 (+cu126), torchvision, monai 1.6 (pip, Pipeline C), numpy, scipy, pandas, scikit-learn, scikit-image, matplotlib, nibabel, SimpleITK.
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## Project
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Longitudinal (repeated-measures) analysis of medical imaging data. Repo is at an early stage — see README.md for any project notes.
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## Conventions
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- All Python commands must run inside the `longitudinal` conda environment.
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- GPU is available (CUDA 12.6); use `torch.device('cuda')` when appropriate. Multi-GPU stages of Pipelines A and C run via `torchrun --standalone --nproc_per_node N` (rank = GPU).
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- No lint/test tooling is configured yet; run scripts directly with `python <script>` from the repo root. |