- 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.
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