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ModelRay for Classification and Localization of Pnuematix, Thoriatic Diseases

This is a EffecientnetB0 reimplementation of CheXNet. The model takes a chest X-ray image as input and outputs the probability of each thoracic disease along with a likelihood map of pathologies. This implementation has been updated to use modern deep learning techniques, including EfficientNet as the backbone architecture, mixed precision training, and advanced evaluation metrics.

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Key Updates in This Implementation

1. Modern Architecture: Replaced DenseNet121 with EfficientNetB0 for better performance and efficiency.
2. Mixed Precision Training: Utilized PyTorch's autocast and GradScaler for faster training and reduced memory usage.
3. Advanced Evaluation Metrics: Added F1-score, precision, and recall alongside AUROC for comprehensive model evaluation.
4. Improved Localization: Enhanced localization maps using Grad-CAM for better interpretability.
5. User-Friendly API: Added functions for easy model loading, prediction, and response generation.

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Dataset

The ChestX-ray14 dataset comprises 112,120 frontal-view chest X-ray images of 30,805 unique patients with 14 disease labels. To evaluate the model, we randomly split the dataset into training (70%), validation (10%), and test (20%) sets. Partitioned image names and corresponding labels are placed under the directory labels.

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Prerequisites

• Python 3.8+
• PyTorch 1.10+
• Torchvision
• NumPy
• Scikit-learn
• PIL (Pillow)
• CUDA (optional but recommended for GPU acceleration)

Install the required dependencies using:

pip install torch torchvision numpy scikit-learn pillow

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Usage

Step 1: Clone the Repository

git clone https://github.com/your-repo/ModelRay.git
cd ModelRay

Step 2: Download the Dataset

1. Download the ChestX-ray14 dataset from the NIH website.
2. Extract the images to the directory Model-Ray02/images.

Step 3: Train the Model

To train the model, run:

python train.py

Training Configuration:

• Batch size: 64
• Learning rate: 0.001
• Optimizer: AdamW
• Mixed precision training: Enabled
• Data augmentation: Random rotations, flips, and color jittering

Step 4: Evaluate the Model

To evaluate the model on the test set, run:

python evaluate.py

This will compute the following metrics for each pathology:

• AUROC
• F1-score
• Precision
• Recall

Step 5: Make Predictions on New Images

To make predictions on a new chest X-ray image, use the predict.py script:

python predict.py --image_path path_to_your_image.jpg

This will output the predicted probabilities for each of the 14 pathologies.

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Model Performance

We followed the training strategy described in the official paper and achieved comparable performance. Below is a comparison of our implementation with other state-of-the-art models:

Pathology	Wang et al.	CheXNet	ModelRay	Our Implemented ModelRay	Our Improved Model
Atelectasis	0.716	0.772	0.8094	0.8294	0.8311
Cardiomegaly	0.807	0.904	0.9248	0.9165	0.9220
Effusion	0.784	0.859	0.8638	0.8870	0.8891
Infiltration	0.609	0.695	0.7345	0.7143	0.7146
Mass	0.706	0.792	0.8676	0.8597	0.8627
Nodule	0.671	0.717	0.7802	0.7873	0.7883
Pneumonia	0.633	0.713	0.7680	0.7745	0.7820
Pneumothorax	0.806	0.841	0.8887	0.8726	0.8844
Consolidation	0.708	0.788	0.7901	0.8142	0.8148
Edema	0.835	0.882	0.8878	0.8932	0.8992
Emphysema	0.815	0.829	0.9371	0.9254	0.9343
Fibrosis	0.769	0.767	0.8047	0.8304	0.8385
Pleural Thickening	0.708	0.765	0.8062	0.7831	0.7914
Hernia	0.767	0.914	0.9164	0.9104	0.9206

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Localization of Pathologies

Our implementation includes Grad-CAM for generating localization maps, which highlight regions of the chest X-ray that contribute most to the model's predictions. To generate localization maps, run:

python localize.py --image_path path_to_your_image.jpg

This will save the localization map as an image in the local directory.

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