🫁 ThoraVis — Thoracic Pathology Classifier

Multi-label chest X-ray classification using PyTorch + HuggingFace Transformers + OpenCV preprocessing
Applied to the NIH ChestX-ray14 dataset (112,120 frontal-view images, 15 label classes)

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📌 Project Overview

ThoraVis is a production-style medical imaging pipeline that:

• Loads and explores the NIH ChestX-ray14 dataset via HuggingFace Datasets
• Applies clinical-grade OpenCV preprocessing (CLAHE enhancement, lung-field normalization, adaptive thresholding)
• Fine-tunes a ViT-B/16 (Vision Transformer) backbone from HuggingFace for multi-label classification
• Implements a custom PyTorch training loop with AUC-ROC tracking per pathology
• Exports Grad-CAM heatmaps (OpenCV overlay) to visualize model attention on X-ray findings
• Achieves competitive AUC ≥ 0.80 on high-prevalence pathologies (Effusion, Atelectasis, Cardiomegaly)

This project was built to concretely demonstrate:

Skill	Demonstrated Via
OpenCV	CLAHE, bilateral filtering, Sobel edge maps, Grad-CAM overlays
PyTorch	Custom Dataset, DataLoader, training loop, loss functions
HuggingFace	datasets for data streaming, transformers ViT model backbone
ML Engineering	AUC-ROC metrics, class imbalance handling, checkpoint management

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🗂️ Repository Structure

thoravis/
├── README.md
├── requirements.txt
├── notebooks/
│   └── 01_thoravis_full_pipeline.ipynb   ← Main Jupyter showcase notebook
├── src/
│   ├── dataset.py       ← HuggingFace + PyTorch Dataset wrapper
│   ├── preprocessing.py ← OpenCV clinical preprocessing pipeline
│   ├── model.py         ← ViT fine-tuning with custom classification head
│   ├── train.py         ← Training loop, AUC tracking, checkpointing
│   ├── evaluate.py      ← Per-pathology AUC-ROC, confusion matrices
│   └── gradcam.py       ← Grad-CAM heatmap generation with OpenCV overlay
├── models/
│   └── .gitkeep
├── results/
│   └── .gitkeep
└── assets/
    └── pipeline_diagram.png

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🧬 Dataset: NIH ChestX-ray14

Property	Value
Source	NIH Clinical Center
HuggingFace Hub	alkzar90/NIH-Chest-X-ray-dataset
Images	112,120 frontal-view PNGs (1024×1024)
Patients	30,805 unique
Labels	15 label classes: 14 diseases + No Finding (multi-label)
License	No restrictions (NIH attribution required)

15 Label Classes (14 diseases + No Finding): No Finding · Atelectasis · Cardiomegaly · Effusion · Infiltration · Mass · Nodule · Pneumonia · Pneumothorax · Consolidation · Edema · Emphysema · Fibrosis · Pleural Thickening · Hernia

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🔬 Pipeline Architecture

Raw X-ray PNG (1024×1024)
        │
        ▼
[OpenCV Preprocessing]
  ├─ Resize to 224×224
  ├─ CLAHE contrast enhancement (clipLimit=3.0, tileGrid=8×8)
  ├─ Bilateral noise filter (preserve edges)
  └─ Normalize to ImageNet stats
        │
        ▼
[HuggingFace ViT-B/16 Backbone]
  └─ google/vit-base-patch16-224-in21k (pretrained)
        │
        ▼
[Custom PyTorch Classification Head]
  └─ Linear(768 → 256) → GELU → Dropout(0.3) → Linear(256 → 15)
        │
        ▼
[Multi-label BCEWithLogitsLoss]
  └─ Weighted by inverse class frequency
        │
        ▼
[Output: 15-dim sigmoid probabilities]

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🚀 Quick Start

1. Install Dependencies

git clone https://github.com/LSaiko/thoravis.git
cd thoravis
pip install -r requirements.txt

2. Run the Notebook

jupyter notebook notebooks/01_thoravis_full_pipeline.ipynb

This single notebook walks through the complete pipeline end-to-end, including:

• Dataset loading and exploration
• OpenCV preprocessing visualization
• Model training (configurable epochs / subset size)
• Evaluation with per-class AUC-ROC
• Grad-CAM heatmap generation

3. Train via Script

# Quick demo run on 5,000 images
python src/train.py --subset 5000 --epochs 5 --batch_size 32

# Full dataset run
python src/train.py --epochs 20 --batch_size 64 --lr 2e-5

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📊 Results (Subset: 5,000 images, 5 epochs)

Full dataset results pending — these are reproducible demo benchmarks

Pathology	AUC-ROC
Cardiomegaly	0.87
Effusion	0.83
Atelectasis	0.81
Pneumothorax	0.79
Consolidation	0.77
Macro Average	0.78

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🔥 Grad-CAM Visualization

Gradient-weighted Class Activation Maps highlight which pixels drove each prediction, overlaid on the original X-ray using OpenCV's applyColorMap.

from src.gradcam import GradCAMVisualizer

viz = GradCAMVisualizer(model, target_layer="vit.encoder.layer[-1]")
heatmap = viz.generate(image_tensor, target_class=2)  # Cardiomegaly
viz.save_overlay(original_image, heatmap, "results/gradcam_cardiomegaly.png")

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🛠️ Key Technical Choices

Why ViT over CNN?
Vision Transformers capture global context across the entire X-ray in a single forward pass — crucial for diffuse pathologies like Cardiomegaly or Edema that span large anatomical regions.

Why CLAHE preprocessing?
X-rays have extreme dynamic range. CLAHE (Contrast Limited Adaptive Histogram Equalization) normalizes local contrast without oversaturating bright bone structures — a standard step in clinical CAD systems.

Handling class imbalance:
No Finding comprises ~53% of labels. We apply inverse-frequency weighting in BCEWithLogitsLoss and use macro AUC-ROC (not accuracy) as the primary metric.

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📦 Requirements

torch>=2.1.0
torchvision>=0.16.0
transformers>=4.38.0
datasets>=2.18.0
opencv-python>=4.9.0
numpy>=1.26.0
scikit-learn>=1.4.0
matplotlib>=3.8.0
tqdm>=4.66.0
accelerate>=0.27.0
Pillow>=10.2.0
jupyter>=1.0.0
ipywidgets>=8.1.0

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📚 Citation

@inproceedings{Wang_2017,
  doi       = {10.1109/cvpr.2017.369},
  year      = 2017,
  publisher = {IEEE},
  author    = {Xiaosong Wang and Yifan Peng and Le Lu and Zhiyong Lu
               and Mohammadhadi Bagheri and Ronald M. Summers},
  title     = {ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks},
  booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition}
}

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📝 License

MIT License — see LICENSE.
Dataset attribution: NIH Clinical Center (required per dataset terms).

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Built to showcase applied medical imaging skills: OpenCV preprocessing · PyTorch training pipelines · HuggingFace model fine-tuning