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sandy45/ChestViT-Explainable-XRay-AI

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App README

Multi-Task Vision Transformer for Chest X-Ray Disease Classification & Explainability

ViT-Base-16 · 14-Disease Multi-Label Classification · Attention Rollout XAI

Fine-tuned on NIH ChestX-ray14 (112,120 frontal chest X-rays)

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🎯 Overview

This project implements an explainable medical AI system for automated chest X-ray analysis. Unlike standard classification models, this system simultaneously:

1.Predicts 14 diseases in parallel (multi-label, not multi-class)
2.Shows WHERE in the X-ray the model is looking via Attention Rollout
3.Compares against published NIH baselines (AUC-ROC per class)
4.Serves a live demo via a Gradio dashboard

The core insight: Vision Transformers (ViTs) divide images into 16×16 patches and route information through 12 attention layers. Attention Rollout traces this information flow back to the input patches — telling us exactly which lung regions drove each disease prediction.

🏗 Architecture

Chest X-Ray Input (PNG, 1024×1024)
           │
           ▼
   ┌───────────────────────┐
   │  CLAHE Preprocessing  │   ← Contrast Limited Adaptive Histogram Equalization
   │  + Albumentations     │   ← Radiologically-realistic augmentation
   └───────────────────────┘
           │ 224×224×3
           ▼
   ┌───────────────────────────────────────────┐
   │          ViT-Base-16                      │
   │  (google/vit-base-patch16-224-in21k)      │
   │                                           │
   │  ┌─────────────────────────────────────┐  │
   │  │  196 Patches (14×14 grid, 16px/ea) │  │
   │  │  + [CLS] token = 197 total tokens  │  │
   │  └─────────────────────────────────────┘  │
   │              ↓                            │
   │  12 Transformer Layers                    │
   │  (12 heads × 64 dim = 768 hidden dim)    │
   │              ↓                            │
   │  [CLS] token → Dropout → Linear(768→14)  │
   └───────────────────────────────────────────┘
           │
           ├─── Logits → Sigmoid → 14 disease probabilities
           │
           └─── Attention weights (12 layers × 12 heads)
                         │
                         ▼
              Attention Rollout Algorithm
              (14×14 patch attention map)
                         │
                         ▼
              224×224 heatmap overlay on X-ray

📊 Results

Disease	ViT AUC	NIH Baseline	Δ AUC
Atelectasis	—	0.7003	—
Cardiomegaly	—	0.8100	—
Effusion	—	0.7585	—
Infiltration	—	0.6614	—
Mass	—	0.6933	—
Nodule	—	0.6689	—
Pneumonia	—	0.6580	—
Pneumothorax	—	0.7993	—
Consolidation	—	0.7032	—
Edema	—	0.8052	—
Emphysema	—	0.8330	—
Fibrosis	—	0.7859	—
Pleural_Thickening	—	0.6835	—
Hernia	—	0.8717	—
MACRO AVERAGE	—	0.7523	—

Results will populate after training. NIH baseline from Wang et al. (2017).

🚀 Quick Start

1. Install Dependencies

bash

# Create virtual environment
python -m venv venv
venv\Scripts\activate   # Windows
# source venv/bin/activate  # Linux/macOS

# Install dependencies
pip install -r requirements.txt

2. Download Dataset

bash

# First: set up Kaggle API credentials
# 1. Go to https://www.kaggle.com → Account → Settings → Create New API Token
# 2. Place kaggle.json at: C:\Users\<YourName>\.kaggle\kaggle.json

# Then download NIH ChestX-ray14 (~42 GB)
python data/download.py

3. Run Unit Tests

bash

python -m pytest tests/ -v

4. Train the Model

bash

# Full training (5 epochs, ~8-12 hours on RTX 3050)
python training/train.py

# Quick smoke test (20% of data)
# Edit config/config.yaml → dataset.train_fraction: 0.2
python training/train.py

Monitor training in real-time:

bash

mlflow ui --backend-store-uri ./experiments/mlflow
# Open http://localhost:5000

5. Launch Demo

bash

# With trained model
python app/gradio_app.py

# DEMO MODE (random weights, for UI preview only)
set DEMO_MODE=1   # Windows
python app/gradio_app.py

📁 Project Structure

.
├── config/
│   └── config.yaml              # All hyperparameters and paths
├── data/
│   ├── download.py              # Kaggle API dataset download
│   ├── preprocessing.py         # CLAHE + Albumentations pipeline
│   ├── dataset.py               # ChestXrayDataset + DataLoaders
│   └── raw/                     # Downloaded dataset (not in git)
├── models/
│   └── vit_model.py             # ViT-Base-16 with multi-label head
├── explainability/
│   └── attention_rollout.py     # Attention Rollout algorithm
├── training/
│   ├── losses.py                # Weighted BCE + Focal Loss
│   ├── train.py                 # Training loop (mixed precision, MLflow)
│   └── evaluate.py              # AUC-ROC per class, ROC plots
├── app/
│   └── gradio_app.py            # Gradio dashboard
├── tests/
│   └── test_modules.py          # Unit tests (no dataset required)
├── checkpoints/                 # Saved model weights (not in git)
├── results/                     # ROC curves, metrics CSV
├── experiments/
│   └── mlflow/                  # MLflow tracking database
├── config_loader.py             # YAML config loader
└── requirements.txt

⚙️ Configuration

All settings are in `config/config.yaml`. Key RTX 3050 settings:

yaml

training:
  batch_size: 8                    # Fits in 4 GB VRAM
  gradient_accumulation_steps: 4   # Effective batch = 32
  mixed_precision: true            # fp16 — mandatory for 4 GB VRAM
  num_epochs: 5

model:
  name: "google/vit-base-patch16-224-in21k"
  gradient_checkpointing: true     # Saves ~30% VRAM

dataset:
  train_fraction: 1.0              # Set 0.2 for quick smoke test

🔥 Attention Rollout: Why Not Grad-CAM?

Method	Grad-CAM	Attention Rollout
Designed for	CNNs	Transformers
Spatial resolution	Depends on last conv layer	14×14 patch grid
Accounts for skip connections	No	Yes (identity matrix)
Computational cost	Requires backward pass	Forward pass only
ViT-specific	No	Yes

Attention Rollout (Abnar & Zuidema, 2020) is mathematically derived from the transformer's own attention mechanism, making it the correct tool for ViT explainability.

🏥 Clinical Context

⚠️ This is a research/educational project, NOT a medical device. Results should not be used for clinical diagnosis without radiologist review.

The NIH ChestX-ray14 dataset has known limitations (Rajpurkar et al., 2018, and others). AUC-ROC is the clinically relevant metric because:

—Accuracy is misleading with class imbalance (>53% "No Finding")
—AUC measures discriminative ability across all thresholds
—Radiologists can set their own confidence threshold per clinical context

📚 References

1.Wang, X. et al. (2017). ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks. CVPR.
2.Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
3.Abnar, S. & Zuidema, W. (2020). Quantifying Attention Flow in Transformers. arXiv:2005.00928.
4.Rajpurkar, P. et al. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv:1711.05225.

🛠 Tech Stack

Tool	Version	Purpose
PyTorch	2.1+	Training framework
HuggingFace Transformers	4.37+	ViT-Base-16 backbone
OpenCV	4.9+	CLAHE preprocessing
Albumentations	1.3+	Image augmentation
scikit-learn	1.4+	AUC-ROC metrics
MLflow	2.10+	Experiment tracking
Gradio	4.x	Demo dashboard
Kaggle API	1.6+	Dataset download