rpotter6298
8a136c71fe
- Created `classification.py` for comparing image-level and patient-level classification results using various CNN models. - Implemented `create_patient_groups.py` to extract features, generate PCA/t-SNE plots, and identify patient groups via K-means clustering. - Added `figure6.py` to generate boxplots for test accuracy across multiple seeds. - Developed `simple_patient_tsne.py` to perform t-SNE visualization of patient groups and save results in a manifest file. - Introduced `simple_patient_manifest.csv` to store patient IDs, classes, image counts, and associated images.
48 lines
410 B
Plaintext
48 lines
410 B
Plaintext
# Archive
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.archive/
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# Python
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__pycache__/
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*.py[cod]
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*.egg-info/
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dist/
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build/
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# Environments
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.env
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.venv
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env/
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venv/
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# IDE
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.idea/
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.vscode/
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*.swp
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*.swo
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# OS
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.DS_Store
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Thumbs.db
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# Sensitive
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*.pem
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*.key
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*.p12
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*.pfx
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credentials*
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secrets*
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*.env.*
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# Data (large feature files — regenerate via scripts)
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features/*.npz
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features/*.npy
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# Outputs
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plots/*.png
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# Jupyter
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.ipynb_checkpoints/
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# Conda
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.python-version
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