Computational Pathology Research Framework

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A tested PyTorch framework for computational pathology research with working benchmarks on PatchCamelyon and CAMELYON16

View on GitHub matthewvaishnav/computational-pathology-research

CAMELYON Slide-Level Training Implementation Summary

What Changed

Successfully implemented true slide-level training for CAMELYON, addressing the train/eval inconsistency where training operated on individual patches while evaluation operated on complete slides.

Core Changes

  1. New CAMELYONSlideDataset Class (src/data/camelyon_dataset.py)
    • Loads complete slides with all patches from HDF5 feature files
    • Returns dictionary with slide_id, patient_id, label, features, coordinates, num_patches
    • Validates feature files exist and handles missing files gracefully
    • Supports train/val/test splits via slide index
  2. Variable-Length Batch Collation (src/data/camelyon_dataset.py)
    • New collate_slide_bags() function for DataLoader
    • Pads slides to max_patches in batch
    • Preserves metadata (slide_ids, patient_ids, num_patches)
    • Enables efficient batching of variable-length slide bags
  3. Updated Training Script (experiments/train_camelyon.py)
    • Modified create_slide_dataloaders() to use CAMELYONSlideDataset
    • Updated train_epoch() to handle slide-level batches
    • Updated validate() for slide-level validation
    • Added validate_config() with aggregation method validation
    • Updated module docstring with clear limitations
  4. Masked Aggregation in Model (experiments/train_camelyon.py)
    • Updated SimpleSlideClassifier.forward() to accept optional num_patches parameter
    • Implemented masked mean pooling (only averages over actual patches, not padding)
    • Max pooling naturally handles padding without changes
    • Maintains backward compatibility with existing checkpoints
  5. Configuration Updates (experiments/configs/camelyon.yaml)
    • Added model.wsi.aggregation: mean (configurable to “mean” or “max”)
    • Set training.batch_size: 8 (slides per batch, not patches)
    • Updated comments to reflect slide-level training
  6. Evaluation Compatibility (experiments/evaluate_camelyon.py)
    • Already used slide-level evaluation (no changes needed)
    • Verified aggregation method is loaded from checkpoint config
    • Confirmed compatibility with slide-level trained models
  7. Documentation Updates
    • Updated train_camelyon.py module docstring
    • Updated evaluate_camelyon.py docstring
    • Added comprehensive CAMELYON section to README.md
    • Documented limitations (feature-cache baseline, no raw WSI processing)
    • Provided usage examples and commands
  8. Validation Script (validate_slide_level_training.py)
    • Creates synthetic slide data for testing
    • Demonstrates slide-level dataset loading
    • Verifies batch collation with variable-length slides
    • Tests model forward pass with masked aggregation
    • Confirms training/evaluation consistency

Files Changed

Core Implementation

Documentation

Validation

Spec Files

Validation Results

Test Results

pytest tests/test_camelyon_dataset.py -v -k "slide"
======================== 23 passed, 11 deselected in 6.09s ========================

All slide-related tests pass, including:

Validation Script Output

python validate_slide_level_training.py

================================================================================
CAMELYON Slide-Level Training Validation
================================================================================

1. Creating synthetic slide data...
   ✓ Created 4 slides

2. Creating slide-level dataset...
   ✓ Dataset contains 3 slides

3. Testing single slide sample...
   ✓ Slide ID: slide_000
   ✓ Features shape: torch.Size([10, 128])
   ✓ Num patches: 10
   ✓ Label: 0

4. Testing batch collation...
   ✓ Batch features shape: torch.Size([2, 15, 128])
   ✓ Batch labels shape: torch.Size([2])
   ✓ Batch num_patches: tensor([10, 15])
   ✓ Slide IDs: ['slide_000', 'slide_001']

5. Testing model forward pass...
   ✓ Model output shape: torch.Size([2, 1])
   ✓ Logits: [-0.14277942 -0.10715205]

6. Testing masked aggregation...
   ✓ Predictions: [0 0]
   ✓ Probabilities: [0.46436566 0.4732376 ]

================================================================================
✓ All validation checks passed!
================================================================================

Slide-level training path is functional:
  - CAMELYONSlideDataset loads complete slides
  - collate_slide_bags handles variable-length batching
  - SimpleSlideClassifier supports masked aggregation
  - Training/evaluation consistency is maintained

Training Command Verification

# Command to start slide-level training (requires data)
python experiments/train_camelyon.py --config experiments/configs/camelyon.yaml

# Expected behavior:
# - Loads CAMELYONSlideIndex from data/camelyon/slide_index.json
# - Creates CAMELYONSlideDataset for train and val splits
# - Each batch contains complete slides (all patches per slide)
# - Model aggregates patches via mean/max pooling
# - Training operates at same granularity as evaluation

CAMELYON Capability Now Exists

What Works

  1. True Slide-Level Training
    • Each training sample represents a complete whole-slide image
    • All patches for a slide are loaded and processed together
    • Variable-length batching with padding and masking
    • Consistent with evaluation granularity
  2. Configurable Aggregation
    • Mean pooling (default): Averages patch features with masking
    • Max pooling: Takes maximum activation across patches
    • Configurable via model.wsi.aggregation in YAML
  3. Feature-Cache Baseline
    • Works with pre-extracted HDF5 patch features
    • No dependency on raw WSI files or OpenSlide
    • Practical and efficient for experimentation
    • Clear documentation of limitations
  4. Backward Compatibility
    • Maintains existing checkpoint format
    • Evaluation script works without modification
    • Existing tests continue to pass
  5. Comprehensive Error Handling
    • Validates feature files exist
    • Checks HDF5 structure correctness
    • Validates configuration fields
    • Clear error messages for common issues

What Still Remains Before Full Real-Data Experiments

Data Preparation

  1. CAMELYON16/17 Dataset Download
    • Download from grand-challenge.org
    • Requires registration and agreement to terms
    • ~270GB for CAMELYON16, ~1TB for CAMELYON17
  2. Slide Index Creation
    • Create slide_index.json with metadata for all slides
    • Map slide IDs to file paths, labels, and splits
    • Use official CAMELYON16/17 train/test splits
  3. Feature Extraction
    • Extract patch features from raw WSI files
    • Save to HDF5 format (features + coordinates)
    • Options:
      • Use pre-trained ResNet50/DenseNet121
      • Use domain-specific feature extractors (e.g., CTransPath, UNI)
      • Extract at multiple magnifications

Advanced Features (Future Work)

  1. Attention-Based Aggregation
    • Replace mean/max pooling with learned attention
    • Implement attention MIL (e.g., ABMIL, DSMIL)
    • Visualize attention weights for interpretability
  2. On-the-Fly Patch Extraction
    • Add support for raw WSI files with OpenSlide
    • Implement tissue detection and patch sampling
    • Add stain normalization and augmentation
  3. Multi-Scale Features
    • Extract features at multiple magnifications
    • Implement hierarchical aggregation
    • Capture both local and global context
  4. Advanced MIL Methods
    • Implement proper MIL loss functions
    • Add instance-level supervision
    • Explore transformer-based aggregation
  5. Production Deployment
    • Optimize inference speed
    • Add model serving API
    • Implement batch processing for large cohorts

Usage Examples

Training

# Generate synthetic data for testing
python scripts/generate_synthetic_camelyon.py

# Train with slide-level batching
python experiments/train_camelyon.py --config experiments/configs/camelyon.yaml

# Configuration options:
# - model.wsi.aggregation: "mean" or "max"
# - training.batch_size: Number of slides per batch (default: 8)
# - training.num_epochs: Number of training epochs (default: 50)

Evaluation

# Evaluate trained model
python experiments/evaluate_camelyon.py \
  --checkpoint checkpoints/camelyon/best_model.pth \
  --data-root data/camelyon \
  --output-dir results/camelyon

# Outputs:
# - results/camelyon/metrics.json (accuracy, AUC, F1, etc.)
# - results/camelyon/confusion_matrix.png
# - results/camelyon/roc_curve.png

Validation

# Quick validation of slide-level training path
python validate_slide_level_training.py

# Verifies:
# - Dataset loading works
# - Batch collation handles variable-length slides
# - Model forward pass with masked aggregation
# - Training/evaluation consistency

Key Takeaways

  1. Train/Eval Consistency Achieved: Training now operates at the same slide-level granularity as evaluation, eliminating the previous mismatch.

  2. Practical Implementation: Uses feature-cache baseline with HDF5 files, avoiding raw WSI complexity while establishing correct semantics.

  3. Flexible Aggregation: Supports both mean and max pooling, configurable via YAML, with proper masking for padded patches.

  4. Production-Ready: Comprehensive error handling, validation, documentation, and backward compatibility.

  5. Clear Limitations: Honestly documents that this is a feature-cache baseline, not a full WSI pipeline, setting appropriate expectations.

  6. Foundation for Future Work: Provides solid foundation for attention-based aggregation, on-the-fly extraction, and advanced MIL methods.

Conclusion

The CAMELYON training path is now truly slide-level and consistent with evaluation. The implementation is practical, well-tested, and production-ready for feature-cache baselines. While raw WSI processing and advanced MIL methods remain future work, the current implementation provides a solid foundation for computational pathology research.