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longitudinal/AGENTS.md
Furen Xiao 77adc2b3af feat(monai): add MONAI pipeline C implementation 
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.
2026-09-26 11:04:20 +08:00

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AGENTS.md

Environment

This project uses a conda environment named longitudinal (Python 3.14).

Activate with:

source /opt/conda/etc/profile.d/conda.sh && conda activate longitudinal

Key packages: torch 2.14 (+cu126), torchvision, monai 1.6 (pip, Pipeline C), numpy, scipy, pandas, scikit-learn, scikit-image, matplotlib, nibabel, SimpleITK.

Project

Longitudinal (repeated-measures) analysis of medical imaging data. Repo is at an early stage — see README.md for any project notes.

Conventions

  • All Python commands must run inside the longitudinal conda environment.
  • 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).
  • No lint/test tooling is configured yet; run scripts directly with python <script> from the repo root.