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.
51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
#!/usr/bin/env python3
|
|
"""
|
|
classification.py — single-seed image-level vs patient-level comparison.
|
|
|
|
Usage:
|
|
conda activate fundus_imaging
|
|
python scripts/classification.py
|
|
"""
|
|
|
|
import os, sys, json
|
|
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
|
|
|
from classes import PatientLeakageClassifier
|
|
|
|
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
|
MODELS = ["VGG16", "DenseNet121", "EfficientNetB1", "MobileNetV2", "ResNet50"]
|
|
SEED = 20
|
|
|
|
clf = PatientLeakageClassifier(
|
|
os.path.join(ROOT, "results", "simple_patient_manifest.csv"),
|
|
os.path.join(ROOT, "features"),
|
|
n_jobs=8)
|
|
|
|
print(f"{'='*60}")
|
|
print(f"IMAGE-LEVEL vs PATIENT-LEVEL (seed={SEED})")
|
|
print(f"{'='*60}")
|
|
|
|
results = []
|
|
for name in MODELS:
|
|
print(f"\n {name} ...")
|
|
img = clf.run(name, SEED, "image")
|
|
pat = clf.run(name, SEED, "patient")
|
|
results.append({"model": name,
|
|
"image_cv": img["cv"], "image_test": img["test"],
|
|
"patient_cv": pat["cv"], "patient_test": pat["test"],
|
|
"drop": img["test"] - pat["test"]})
|
|
print(f" Image: CV={img['cv']:.4f} Test={img['test']:.4f}")
|
|
print(f" Patient: CV={pat['cv']:.4f} Test={pat['test']:.4f}")
|
|
print(f" Drop: {img['test'] - pat['test']:.4f}")
|
|
|
|
print(f"\n {'Model':<18s} {'Img-CV':>8s} {'Img-Test':>9s} "
|
|
f"{'Pat-CV':>8s} {'Pat-Test':>9s} {'Drop':>7s}")
|
|
print(f" {'-'*54}")
|
|
for r in results:
|
|
print(f" {r['model']:<18s} {r['image_cv']:>8.4f} {r['image_test']:>9.4f} "
|
|
f"{r['patient_cv']:>8.4f} {r['patient_test']:>9.4f} {r['drop']:>7.4f}")
|
|
|
|
with open(os.path.join(ROOT, "results", "classification_results.json"), "w") as f:
|
|
json.dump(results, f, indent=2)
|
|
print(f"\nDONE")
|