2026001
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rpotter6298
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#!/usr/bin/env python3
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"""
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classification_thumbnail.py — Run the classification pipeline using the
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manuscript's thumbnail-based K-means patient manifest.
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Saves results alongside the feature-based results for comparison.
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Usage:
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conda activate fundus_imaging
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python scripts/classification_thumbnail.py
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"""
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import os, sys, csv, json, re
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import numpy as np
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(
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os.path.abspath(__file__)))))
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from classes import PatientIdentifier, PatientLeakageClassifier
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# ---------------------------------------------------------------------------
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# Config
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# ---------------------------------------------------------------------------
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ROOT = os.path.dirname(os.path.dirname(os.path.dirname(
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os.path.abspath(__file__))))
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DATASET_PATH = os.path.join(os.path.dirname(ROOT),
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"The IQ-OTHNCCD lung cancer dataset")
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FEATURES_DIR = os.path.join(ROOT, "features")
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RESULTS_DIR = os.path.join(ROOT, "results")
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SEED = 20
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MODELS = ["VGG16", "DenseNet121", "EfficientNetB1", "MobileNetV2", "ResNet50"]
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# Known patient counts per class
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PATIENT_COUNTS = {
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"Bengin cases": 15,
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"Malignant cases": 40,
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"Normal cases": 55,
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}
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# ---------------------------------------------------------------------------
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# Step 1: Build thumbnail-based patient manifest
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# ---------------------------------------------------------------------------
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print("=" * 60)
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print("Building thumbnail-based patient manifest")
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print("=" * 60)
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# Collect all image filenames and class labels (same order as feature extraction)
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image_paths, all_labels, all_fnames = [], [], []
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for class_name in sorted(os.listdir(DATASET_PATH)):
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class_path = os.path.join(DATASET_PATH, class_name)
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if not os.path.isdir(class_path):
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continue
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for file in sorted(os.listdir(class_path)):
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if file.lower().endswith((".png", ".jpg", ".jpeg")):
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image_paths.append(os.path.join(class_path, file))
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all_labels.append(class_name)
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all_fnames.append(file)
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print(f"Found {len(image_paths)} images across "
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f"{len(set(all_labels))} classes")
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# Run thumbnail K-means
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labels_arr = np.array(all_labels)
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fnames_arr = np.array(all_fnames)
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identifier = PatientIdentifier(
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patient_estimates=PATIENT_COUNTS, random_state=42)
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groups = identifier.identify_from_thumbnails(
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DATASET_PATH, fnames_arr, labels_arr)
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# Build manifest
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manifest = identifier.build_assignment_dict(groups, fnames_arr, labels_arr)
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# Save manifest
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manifest_path = os.path.join(RESULTS_DIR, "thumbnail_patient_manifest.csv")
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def f2n(fname):
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m = re.search(r"\((\d+)\)", fname)
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num = int(m.group(1)) if m else None
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for cls_key, prefix in [
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("Bengin cases", "B"), ("Malignant cases", "M"), ("Normal cases", "N"),
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]:
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if fname.startswith(cls_key.rstrip("s")):
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return f"{prefix}_{num:03d}" if num else fname
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return fname
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with open(manifest_path, "w", newline="") as f:
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writer = csv.writer(f)
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writer.writerow(["patient_id", "class", "n_images", "images"])
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for pid in sorted(manifest.keys()):
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imgs = manifest[pid]
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short_names = [f2n(img) for img in imgs]
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cls = pid.split("_")[0] # "benign_0" → "benign"
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cls = {"benign": "Benign", "malig": "Malignant",
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"normal": "Normal"}.get(cls, cls)
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writer.writerow([pid, cls, len(imgs), ";".join(short_names)])
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print(f"Manifest saved → {manifest_path}")
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print(f" {len(manifest)} estimated patients")
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# Per-class stats
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for cls in ["Bengin cases", "Malignant cases", "Normal cases"]:
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short_cls = {"Bengin cases": "benign", "Malignant cases": "malig",
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"Normal cases": "normal"}[cls]
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cls_patients = {k: v for k, v in manifest.items()
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if k.startswith(short_cls)}
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n_pat = len(cls_patients)
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n_img = sum(len(v) for v in cls_patients.values())
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print(f" {cls}: {n_pat} patients, {n_img} images")
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# ---------------------------------------------------------------------------
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# Step 2: Run classification with thumbnail manifest
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# ---------------------------------------------------------------------------
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print(f"\n{'=' * 60}")
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print("Running classification (thumbnail manifest, seed=20)")
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print("=" * 60)
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clf = PatientLeakageClassifier(manifest_path, FEATURES_DIR, n_jobs=6)
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results = []
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for model_name in MODELS:
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print(f"\n {model_name} ...", flush=True)
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img = clf.run(model_name, SEED, "image")
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pat = clf.run(model_name, SEED, "patient")
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results.append({
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"model": model_name,
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"manifest": "thumbnail",
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"image_cv": img["cv"],
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"image_test": img["test"],
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"patient_cv": pat["cv"],
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"patient_test": pat["test"],
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"drop": img["test"] - pat["test"],
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})
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print(f" Image: CV={img['cv']:.4f} Test={img['test']:.4f}")
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print(f" Patient: CV={pat['cv']:.4f} Test={pat['test']:.4f} "
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f"Drop={img['test'] - pat['test']:.4f}")
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# ---------------------------------------------------------------------------
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# Save results
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# ---------------------------------------------------------------------------
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out_path = os.path.join(RESULTS_DIR, "classification_thumbnail.json")
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with open(out_path, "w") as f:
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json.dump(results, f, indent=2)
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# Also load feature-based results for side-by-side comparison
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feat_path = os.path.join(RESULTS_DIR, "classification_results.json")
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if os.path.exists(feat_path):
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with open(feat_path) as f:
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feat_results = json.load(f)
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print(f"\n{'=' * 80}")
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print("COMPARISON: Feature-based (PCA-50) vs Thumbnail K-means")
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print(f"{'=' * 80}")
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print(f"{'Model':<18s} {'Feat Image':>11s} {'Thumb Image':>12s} "
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f"{'Feat Pat':>9s} {'Thumb Pat':>10s} {'Feat Drop':>10s} "
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f"{'Thumb Drop':>11s}")
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print("-" * 78)
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for tr, fr in zip(results, feat_results):
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assert tr["model"] == fr["model"]
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print(f"{tr['model']:<18s} {fr['image_test']:>11.4f} "
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f"{tr['image_test']:>12.4f} {fr['patient_test']:>9.4f} "
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f"{tr['patient_test']:>10.4f} {fr['drop']:>10.4f} "
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f"{tr['drop']:>11.4f}")
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print(f"\nSaved → {out_path}")
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print("DONE")
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