DACNet: Reproduction and Extension of CheXNet for Chest X-ray Classification

This repository contains:

1. a reproduction of the CheXNet DenseNet-121 model on the NIH ChestX-ray14 dataset, and
2. an extension (DACNet) designed to improve performance under class imbalance.

It also includes a Vision Transformer baseline and a Streamlit demo for inference.

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Reproduction Scope

This work reproduces key components of:

CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
Rajpurkar et al., 2017 (arXiv:1711.05225)

What is reproduced

• DenseNet-121 architecture
• Multi-label classification (14 thoracic diseases)
• Patient-level dataset splitting
• Evaluation using AUC-ROC and F1 score

Known differences from the original work

• No access to the original expert-labeled test set
• Labels are based on the publicly available NIH dataset
• Some implementation details are inferred due to lack of official code

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Repository Structure

DacNet/
├── scripts/
│   ├── replicate_chexnet.py
│   ├── Dacnet.py
│   ├── vit_transformer.py
├── XRay_app/
├── reproducibility/
│   └── test_run.sh
├── project_EDA.ipynb
├── requirements.txt

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Dataset

This project uses the NIH ChestX-ray14 dataset.

• ~112,000 frontal chest X-rays
• 30,805 patients
• 14 disease labels

Download from: https://www.kaggle.com/datasets/nih-chest-xrays/data

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Dataset Setup

Your directory must match what the scripts expect.

Example structure:

data/
├── Data_Entry_2017.csv
├── images_001/
├── images_002/
...
├── images_012/

If your dataset is stored elsewhere, update the path in the scripts:

CONFIG["data_dir"] = "/path/to/your/data"

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Reproducible Environment (Docker)

We provide a Docker environment to improve reproducibility.

Build the container

docker build -t dacnet-env .

Run the container

docker run --gpus all -it --rm -v $(pwd):/workspace/DacNet dacnet-env

Notes:

• Requires CUDA-compatible GPU
• ≥8GB VRAM recommended
• If no GPU, remove --gpus all

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Quick Verification

Before running full training, verify the setup:

bash reproducibility/test_run.sh

This checks:

• dataset loading
• model initialization
• training loop execution
  If this step fails, verify dataset paths and file structure before proceeding to full training.

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Running the Models

Once inside the Docker container, you can execute the training scripts.

1. Reproduction Baseline (CheXNet)

python scripts/replicate_chexnet.py

2. DACNet (Improved CNN)

python scripts/Dacnet.py

3. Vision Transformer

python scripts/vit_transformer.py

If you encounter CUDA memory errors, reduce batch size in the scripts.

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Evaluation results (AUC and F1) will print to the console. If WANDB is configured, it will log automatically; otherwise, the container defaults to offline mode. The best model checkpoint will be saved in a models/<run_id> folder.

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Models

replicate_chexnet.py (Baseline)

DenseNet-121 reproduction of CheXNet.

• Multi-label classification (14 diseases)
• BCEWithLogitsLoss
• Patient-level split
• Evaluated using AUC and F1

Dacnet.py (DACNet)

Improved CNN designed for class imbalance.

• DenseNet-121 backbone
• Focal Loss
• Per-class threshold tuning
• Improved F1 performance

vit_transformer.py (ViT)

Transformer-based baseline.

• ViT-Base architecture
• Multi-label classification
• Compared against CNN approaches

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Performance (Test AUC per Disease)

Pathology	original CheXNet	Dacnet.py	vit_transformer.py	replicate_chexnet.py
Atelectasis	0.8094	0.817	0.774	0.762
Cardiomegaly	0.9248	0.932	0.89	0.922
Consolidation	0.7901	0.783	0.789	0.746
Edema	0.8878	0.896	0.876	0.864
Effusion	0.8638	0.905	0.857	0.883
Emphysema	0.9371	0.963	0.828	0.85
Fibrosis	0.8047	0.814	0.772	0.766
Hernia	0.9164	0.997	0.872	0.925
Infiltration	0.7345	0.708	0.7	0.673
Mass	0.8676	0.919	0.783	0.824
Nodule	0.7802	0.789	0.673	0.646
Pleural Thickening	0.8062	0.801	0.766	0.756
Pneumonia	0.768	0.74	0.713	0.656
Pneumothorax	0.8887	0.875	0.821	0.827

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Average metrics across all diseases for each model

Metric	DacNet	ViT Transformer	Replicate CheXNet
Loss	0.0416	0.1589	0.1661
AUC	0.8527	0.7940	0.7928
F1	0.3861	0.1114	0.0763

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F1 Score Comparison for Each Model

Disease	DacNet	ViT Transformer	Replicate CheXNet
AVERAGE	0.386	0.111	0.076
Atelectasis	0.421	0.127	0.026
Cardiomegaly	0.532	0.264	0.423
Consolidation	0.226	0	0
Edema	0.286	0.004	0
Effusion	0.623	0.427	0.459
Emphysema	0.516	0.079	0
Fibrosis	0.127	0	0
Hernia	0.750	0	0
Infiltration	0.395	0.193	0.061
Mass	0.477	0.213	0.079
Nodule	0.352	0.041	0
Pleural Thickening	0.258	0	0
Pneumonia	0.082	0	0
Pneumothorax	0.360	0.211	0.021

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Test-Images

Folder that contains labeled chest X-ray PNG files for the user to easily download and test on the Hugging Face Streamlit app.

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project_EDA.ipynb

Jupyter Notebook that conducts Exploratory Data Analysis such as how many different diseases are present in the dataset and the proportion of each disease in the dataset.

Methodology

• Data Preprocessing: Resize, normalize, and augment X-ray images
• Model Selection: CNN and Transformer variants
• Training & Validation: Patient-level splits, loss monitoring
• Evaluation: Per-class AUC-ROC and F1 metrics
• Comparison: Benchmarks against original CheXNet results

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Demo

Try the model here:
https://huggingface.co/spaces/cfgpp/DACNet

Features:

• image upload
• multi-label prediction
• Grad-CAM visualization

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Limitations

• Results rely on NIH labels (not expert annotations)
• Some implementation details are inferred
• Performance may vary across environments
• Dataset path must be manually configured

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Citation

If you use this repository:

An Open-Source Reproduction and Enhancement of CheXNet for Chest X-ray Disease Classification
https://arxiv.org/abs/2505.06646

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Acknowledgments

Rajpurkar et al.
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
https://arxiv.org/abs/1711.05225