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

CAMELYON16 Training Path Status

Overview

The CAMELYON16 training path is now functional with synthetic data. This document describes what exists, what works, and what remains to be implemented.

What Exists

✅ Complete Training Pipeline

Training Script: experiments/train_camelyon.py

Evaluation Script: experiments/evaluate_camelyon.py

Model Architecture: SimpleSlideClassifier

Dataset Implementation: src/data/camelyon_dataset.py

✅ Synthetic Data Generator

Script: scripts/generate_synthetic_camelyon.py

Default Synthetic Dataset:

✅ Configuration Files

Full Training: experiments/configs/camelyon.yaml

Quick Test: experiments/configs/camelyon_quick_test.yaml

✅ Tests

Config Tests: tests/test_camelyon_config.py (13 tests)

Generator Tests: tests/test_generate_synthetic_camelyon.py (5 tests)

Evaluation Tests: tests/test_evaluate_camelyon.py (7 tests)

Smoke Test Results

Training (1 epoch on synthetic data):

python experiments/train_camelyon.py --config experiments/configs/camelyon_quick_test.yaml

Results:

Evaluation (test split on synthetic data):

python experiments/evaluate_camelyon.py --checkpoint checkpoints/camelyon_quick_test/best_model.pth --split test

Results:

Status: ✅ Complete training → evaluation workflow works end-to-end

What Still Needs Implementation

❌ Real Data Processing

WSI Preprocessing Pipeline:

Feature Extraction:

Annotation Processing:

❌ Advanced Model Architectures

Attention-Based Aggregation:

Graph-Based Methods:

❌ Evaluation and Analysis

Interpretability:

Statistical Analysis:

❌ Real Dataset Integration

CAMELYON16:

CAMELYON17:

Usage Instructions

Generate Synthetic Data

# Default: 20 train, 5 val, 5 test slides
python scripts/generate_synthetic_camelyon.py

# Custom configuration
python scripts/generate_synthetic_camelyon.py \
  --output-dir ./data/camelyon \
  --num-train 50 \
  --num-val 10 \
  --num-test 10 \
  --num-patches 200 \
  --feature-dim 2048

Train Model

# Quick 1-epoch smoke test
python experiments/train_camelyon.py \
  --config experiments/configs/camelyon_quick_test.yaml

# Full training (50 epochs)
python experiments/train_camelyon.py \
  --config experiments/configs/camelyon.yaml

Evaluate Model

# Evaluate on test split
python experiments/evaluate_camelyon.py \
  --checkpoint checkpoints/camelyon_quick_test/best_model.pth \
  --split test \
  --output-dir results/camelyon_quick_test

# Evaluate on validation split
python experiments/evaluate_camelyon.py \
  --checkpoint checkpoints/camelyon/best_model.pth \
  --split val \
  --output-dir results/camelyon

# Use max pooling aggregation
python experiments/evaluate_camelyon.py \
  --checkpoint checkpoints/camelyon/best_model.pth \
  --split test \
  --aggregation max \
  --output-dir results/camelyon_max

Run Tests

# Config tests
pytest tests/test_camelyon_config.py -v

# Generator tests
pytest tests/test_generate_synthetic_camelyon.py -v

# Evaluation tests
pytest tests/test_evaluate_camelyon.py -v

# All CAMELYON tests
pytest tests/test_camelyon*.py tests/test_generate_synthetic_camelyon.py tests/test_evaluate_camelyon.py -v

File Structure

computational-pathology-research/
├── experiments/
│   ├── train_camelyon.py              # Training script
│   ├── evaluate_camelyon.py           # Evaluation script
│   └── configs/
│       ├── camelyon.yaml              # Full training config
│       └── camelyon_quick_test.yaml   # Quick test config
├── scripts/
│   └── generate_synthetic_camelyon.py # Synthetic data generator
├── src/
│   └── data/
│       ├── camelyon_dataset.py        # Dataset classes
│       └── camelyon_annotations.py    # Annotation processing (stub)
├── tests/
│   ├── test_camelyon_config.py        # Config tests
│   ├── test_generate_synthetic_camelyon.py  # Generator tests
│   └── test_evaluate_camelyon.py      # Evaluation tests
└── data/
    └── camelyon/                      # Data directory (gitignored)
        ├── slide_index.json           # Slide metadata
        └── features/                  # HDF5 feature files
            ├── slide_000.h5
            ├── slide_001.h5
            └── ...

Comparison to PCam Path

Feature PCam CAMELYON
Training Script ✅ Complete ✅ Complete
Evaluation Script ✅ Complete ✅ Complete
Synthetic Data ✅ 700 samples ✅ 30 slides (3000 patches)
Real Data Support ✅ H5 format ❌ Requires WSI preprocessing
Model Architecture ✅ ResNet + Transformer ✅ SimpleSlideClassifier
Benchmark Results ✅ 94% accuracy ✅ 100% acc (synthetic)
Interpretability ✅ Full suite ❌ Not implemented
Comparison Runner ✅ Complete ❌ Not implemented

Next Steps

Priority 1: Comparison Runner (Following PCam Pattern)

Priority 2: Interpretability

Priority 3: Real Data Support

Priority 4: Advanced Models

Priority 5: Reproducibility

Important Caveats

⚠️ Synthetic Data Only: Current results are on synthetic data with artificially separated classes. Real CAMELYON16 data will be significantly more challenging.

⚠️ Simple Baseline: SimpleSlideClassifier is a minimal baseline. State-of-the-art methods use attention mechanisms, graph neural networks, or transformer architectures.

⚠️ No Clinical Validation: This is a research framework for testing architectural ideas, not a clinical tool.

⚠️ Patch-Level Workaround: Current implementation treats patches independently rather than true slide-level batching. This works but is not optimal for memory efficiency.

Commits

References