Skip to content

PCAM_REAL_RESULTS.md

Latest commit

 

History

History
205 lines (159 loc) · 7.04 KB

PCAM_REAL_RESULTS.md

File metadata and controls

205 lines (159 loc) · 7.04 KB

PatchCamelyon Real Dataset Results

Date: 2026-04-09
Status: ✅ COMPLETE
Training Time: ~6 hours (20 epochs)
Hardware: RTX 4070 Laptop (8GB VRAM)

Executive Summary

Successfully trained and evaluated a binary classification model on the full PatchCamelyon (PCam) dataset, achieving 85.26% test accuracy and 0.9394 AUC on the complete 32,768-sample test set with bootstrap confidence intervals.

Final Metrics with Bootstrap Confidence Intervals

Test Set Performance (32,768 samples)

Metric Value 95% CI Lower 95% CI Upper
Accuracy 85.26% 84.83% 85.63%
AUC 0.9394 0.9369 0.9418
F1 Score 0.8507 0.8464 0.8543
Precision (macro) 0.8718 0.8680 0.8751
Recall (macro) 0.8526 0.8486 0.8561

Bootstrap Configuration: 1,000 samples, 95% confidence level, random_state=42

Per-Class Performance

Class Precision Recall F1
Class 0 (Normal) 0.787 0.966 0.868
Class 1 (Tumor) 0.956 0.739 0.834

Confusion Matrix

              Predicted
              Normal  Tumor
Actual Normal  15,837    554
       Tumor    4,276 12,101

Analysis:

  • Model correctly classified 27,938/32,768 test samples (85.26%)
  • 554 false positives (normal tissue classified as tumor) - 3.4% of normals
  • 4,276 false negatives (tumors missed) - 26.1% of tumors
  • High precision for tumor detection (95.6%) but moderate recall (73.9%)
  • Conservative toward normal classification

Dataset Details

  • Train Samples: 262,144
  • Val Samples: 32,768
  • Test Samples: 32,768
  • Image Size: 96×96 RGB patches
  • Classes: Binary (0=normal, 1=metastatic tumor)
  • Source: Full PatchCamelyon dataset

Model Architecture

  • Feature Extractor: ResNet-18 (pretrained on ImageNet)
  • Total Parameters: ~12M
  • Embedding Dimension: 256
  • Architecture: ResNet-18 → Transformer Encoder → Classification Head

Training Configuration

training:
  num_epochs: 20
  batch_size: 128
  learning_rate: 1e-3
  weight_decay: 1e-4
  optimizer: AdamW
  use_amp: true  # Mixed precision training
  
hardware:
  device: CUDA (RTX 4070 Laptop)
  vram: 8GB
  training_time: ~6 hours

Performance Characteristics

  • Training Time: ~6 hours (20 epochs)
  • Inference Time: ~2.5 seconds (32,768 samples)
  • Throughput: ~13,000 samples/second
  • Hardware: RTX 4070 Laptop (8GB VRAM)
  • Memory: <8GB VRAM during training

Comparison to Published Baselines

Method Test Accuracy Test AUC Notes
Baseline CNN ~70% ~0.85 Simple CNN
ResNet-18 ~85% ~0.92 Standard baseline
DenseNet-121 ~89% ~0.95 Strong baseline
The Model 85.26% 0.9394 Full PCam dataset

Note: The results are competitive with ResNet-18 baselines and demonstrate the framework's capability on real pathology data.

Statistical Validation

Bootstrap Methodology

  • Samples: 1,000 bootstrap resamples
  • Confidence Level: 95%
  • Method: Percentile method
  • Random State: 42 (reproducible)

Confidence Interval Interpretation

  • Accuracy CI (84.83% - 85.63%): There is 95% confidence the true accuracy lies in this range
  • AUC CI (0.9369 - 0.9418): Tight interval indicates stable discriminative performance
  • F1 CI (0.8464 - 0.8543): Balanced precision-recall tradeoff is consistent

Artifact Paths

Results

  • results/pcam_real/metrics.json - Complete evaluation metrics with bootstrap CIs
  • results/pcam_real/confusion_matrix.png - Confusion matrix visualization
  • results/pcam_real/roc_curve.png - ROC curve (AUC=0.9394)

What This Proves

✅ Framework Capabilities Demonstrated

  1. Scales to full dataset: Successfully trained on 262K samples
  2. Real pathology data: Works on actual PCam dataset, not synthetic
  3. Competitive performance: Achieves results comparable to published baselines
  4. Statistical rigor: Bootstrap confidence intervals for robust evaluation
  5. Production-scale inference: Processes 32K test samples efficiently
  6. GPU optimization: Leverages mixed precision training for efficiency

✅ Technical Validation

  • ResNet-18 feature extraction works on real pathology patches
  • Training converges on large-scale dataset
  • Evaluation metrics are statistically validated
  • Performance is reproducible with confidence intervals

Limitations and Caveats

Model Limitations

  1. Single-patch classification: No multi-patch aggregation
  2. No spatial context: Treats each patch independently
  3. Moderate recall: 73.9% recall means ~26% of tumors are missed
  4. Class imbalance handling: Could be improved for better recall

Evaluation Limitations

  1. Single train/test split: No cross-validation performed
  2. No failure analysis: Haven't analyzed misclassified cases in detail
  3. No comparison to pathologists: Human performance baseline not established

Next Steps for Further Validation

To strengthen claims further:

  1. Cross-validation: Multiple train/test splits for robustness
  2. Failure analysis: Qualitative analysis of misclassified samples
  3. Hyperparameter tuning: Optimize for better recall
  4. Ensemble methods: Combine multiple models for improved performance
  5. Test on CAMELYON16: Evaluate generalization to slide-level classification
  6. Compare to pathologists: Establish human performance baseline

Reproducibility

Commands Used

Training:

python experiments/train_pcam.py \
  --config experiments/configs/pcam_rtx4070_laptop.yaml \
  --data-root data/pcam_real \
  --output-dir checkpoints/pcam_real

Evaluation with Bootstrap CI:

python experiments/evaluate_pcam.py \
  --checkpoint checkpoints/pcam_real/best_model.pth \
  --data-root data/pcam_real \
  --output-dir results/pcam_real \
  --batch-size 64 \
  --bootstrap-samples 1000

Configuration

  • Seed: 42 (fixed for reproducibility)
  • PyTorch: 2.0+
  • CUDA: 11.8
  • Platform: Windows 10, RTX 4070 Laptop
  • Python: 3.12

Conclusion

This benchmark successfully demonstrates that the computational pathology framework:

  1. Scales to production datasets: Handles 262K training samples efficiently
  2. Achieves competitive performance: 85.26% accuracy, 0.9394 AUC on full PCam test set
  3. Provides statistical rigor: Bootstrap confidence intervals for robust evaluation
  4. Leverages GPU acceleration: Efficient training with mixed precision
  5. Produces reproducible results: Fixed seeds and documented configuration

This represents a validated scientific benchmark on real pathology data with proper statistical evaluation.

Status: Scientific benchmark complete ✅
Dataset: Full PatchCamelyon (262K train, 32K test) ✅
Statistical validation: Bootstrap confidence intervals ✅
Clinical validation: Not applicable (research framework) ⚠️
Production ready: Requires clinical validation ⚠️