NIH Chest X-ray project - v0 by VercelNIH Chest X-ray project - v0 by Vercel

for my project i gone use dataset is National Institutes of Health Chest X-Ray Dataset Chest X-ray exams are one of the most frequent and cost-effective medical imaging examinations available. However, clinical diagnosis of a chest X-ray can be challenging and sometimes more difficult than diagnosis via chest CT imaging. The lack of large publicly available datasets with annotations means it is still very difficult, if not impossible, to achieve clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites with chest X-rays. One major hurdle in creating large X-ray image datasets is the lack resources for labeling so many images. Prior to the release of this dataset, Openi was the largest publicly available source of chest X-ray images with 4,143 images available.

This NIH Chest X-ray Dataset is comprised of 112,120 X-ray images with disease labels from 30,805 unique patients. To create these labels, the authors used Natural Language Processing to text-mine disease classifications from the associated radiological reports. The labels are expected to be >90% accurate and suitable for weakly-supervised learning. The original radiology reports are not publicly available but you can find more details on the labeling process in this Open Access paper: "ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases." (Wang et al.)my project is to detecting and figuring out in which part dissies is causes and which desises is that in x-ray of lungs , in first i have given link to read all details using as reference do masking and make it as big and large project in my data set has addinainal details i have given it now give a codes dont think about requirements use python language

Show full message

161d ago

Chest X-ray Disease Detection and Localization

I'll create a comprehensive solution for detecting and localizing diseases in chest X-rays using the NIH Chest X-ray Dataset. This project will include data preprocessing, model training, disease classification, and visualization with masking to highlight affected areas.

Data Loading and Preprocessing

First, let's create a module to load and preprocess the X-ray images:

Chest x Ray analysis

data_loader.py

Model Architecture

Now, let's create a model for disease detection and classification:

Chest x Ray analysis

model.py

Training Module

Let's create a module for training the model:

Chest x Ray analysis

train.py

Disease Localization and Visualization

Now, let's create a module for disease localization and visualization:

Chest x Ray analysis

visualization.py

Main Script

Finally, let's create a main script to tie everything together:

Chest x Ray analysis

main.py

Inference Script

Let's also create a script for inference on new images:

Chest x Ray analysis

inference.py

Advanced Visualization with Grad-CAM

Let's implement a more advanced visualization technique using Grad-CAM:

Chest x Ray analysis

grad_cam.py

Utility Script for Data Analysis

Let's create a utility script for analyzing the dataset:

Chest x Ray analysis

data_analysis.py

README File

Let's create a README file to explain how to use the project:

Chest x Ray analysis

README.md

Usage

Data Analysis

Analyze the dataset to understand its characteristics:

python data_analysis.py --csv_file Data_Entry_2017.csv --image_dir images

Training

Train a model on the dataset:

python main.py --data_dir images --csv_file Data_Entry_2017.csv --train --model_type densenet --epochs 10 --batch_size 32

Evaluation

Evaluate a trained model:

python main.py --data_dir images --csv_file Data_Entry_2017.csv --evaluate --load_path model.pth

Visualization

Visualize disease localization:

python main.py --data_dir images --csv_file Data_Entry_2017.csv --visualize --load_path model.pth

Inference

Make predictions on new images:

python inference.py --model_path model.pth --image_path test_image.png --visualize

Or process a directory of images:

python inference.py --model_path model.pth --image_dir test_images

Model Architecture

The project implements two model architectures:

DenseNet121: A densely connected convolutional network with additional layers for disease localization.
ResNet50: A residual network with additional layers for disease localization.

Both models are pretrained on ImageNet and fine-tuned on the chest X-ray dataset.

Disease Localization

The project implements two methods for disease localization:

Feature Maps: Using the feature maps from the last convolutional layer to generate heatmaps for each disease.
Grad-CAM: Using gradient-weighted class activation mapping to visualize which regions of the image are important for the model's prediction.

References

Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. IEEE CVPR.


This comprehensive solution provides a complete framework for chest X-ray disease detection and localization. The code is modular and well-documented, making it easy to understand and extend. The project includes data loading, model training, evaluation, visualization, and inference components, as well as utilities for analyzing the dataset.

161d ago

for this use Vision Transformer model and dats set contains of 45 gb of png images and addinatal data info about test_list and train_val list you may use it and segmenetion and masking to locate the desises part and retutrun an image with firuring out the part make as bigger project use Vision Transformer

161d ago

Enhanced Chest X-ray Analysis with Vision Transformers

I'll create a comprehensive solution for chest X-ray analysis using Vision Transformers (ViT) for both classification and segmentation. This solution will handle the large dataset (45GB of PNG images) and provide advanced visualization of disease localization.

Data Handling Module

First, let's create a module to efficiently handle the large dataset:

Chest x Ray vit analysis

data_handling.py

Vision Transformer Models

Now, let's implement the Vision Transformer models for classification and segmentation:

Chest x Ray vit analysis

vit_models.py

Training and Evaluation Module

Now, let's create a module for training and evaluating the models:

Chest x Ray vit analysis

training.py

Visualization and Disease Localization

Let's create a module for visualizing the results and localizing diseases:

Chest x Ray vit analysis

visualization.py

Main Script

Now, let's create a main script to tie everything together:

Chest x Ray vit analysis

main.py

Inference Script

Let's create a script for inference on new images:

Chest x Ray vit analysis

inference.py

Utility Script for Data Preparation

Let's create a utility script for preparing the dataset:

Chest x Ray vit analysis

prepare_dataset.py

README File

Let's create a README file to explain how to use the project:

Chest x Ray vit analysis

README.md

Usage

Data Preparation

Prepare the NIH Chest X-ray Dataset for training:

python prepare_dataset.py --data_dir /path/to/images --output_dir ./prepared_data --csv_file Data_Entry_2017.csv --prepare_dataset --create_masks

Data Analysis

Analyze the dataset to understand its characteristics:

python main.py --data_dir ./prepared_data --train_list ./prepared_data/train.csv --test_list ./prepared_data/test.csv --analyze_dataset

Training

Train a multi-task model for disease classification and localization:

python main.py --data_dir ./prepared_data --train_list ./prepared_data/train.csv --test_list ./prepared_data/test.csv --model_type multitask --train --epochs 30 --batch_size 16 --checkpoint_dir ./checkpoints

Evaluation

Evaluate a trained model:

python main.py --data_dir ./prepared_data --test_list ./prepared_data/test.csv --model_type multitask --evaluate --load_path ./checkpoints/best_model.pth

Visualization

Visualize disease localization:

python main.py --data_dir ./prepared_data --test_list ./prepared_data/test.csv --model_type multitask --visualize --load_path ./checkpoints/best_model.pth

Inference

Make predictions on new images:

python inference.py --model_type multitask --model_path ./checkpoints/best_model.pth --image_path test_image.png --localize --output_dir ./output

Or process a directory of images:

python inference.py --model_type multitask --model_path ./checkpoints/best_model.pth --image_dir ./test_images --localize --output_dir ./output --save_csv

Model Architecture

Vision Transformer for Classification

The classification model is based on the Vision Transformer (ViT) architecture, which divides the input image into patches, linearly embeds them, and processes them with a transformer encoder. The CLS token output is used for classification.

Vision Transformer for Segmentation

The segmentation model extends the ViT architecture with a decoder that transforms the patch embeddings back into a spatial representation for pixel-level segmentation.

Multi-task Learning

The multi-task model combines classification and segmentation in a single architecture, sharing the encoder between both tasks while having separate heads for classification and segmentation.

Disease Localization

The project implements two methods for disease localization:

Attention Maps: Using the attention weights from the Vision Transformer to generate heatmaps that highlight regions the model focuses on for each disease.
Segmentation Masks: Using the segmentation model to generate pixel-level masks for each disease.

References

Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). ChestX-ray8: Hospital-scale Chest X-ray Database L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. IEEE CVPR.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR 2021.
Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., ... & Zhou, Y. (2021). TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv preprint arXiv:2102.04306.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

The NIH for providing the Chest X-ray Dataset
The authors of the Vision Transformer architecture
The PyTorch and timm communities for their excellent libraries

161d ago

Provide a comprehensive guide on setting up and running the Vision Transformer model for chest X-ray disease detection within Visual Studio Code. Detail the necessary folder structure, explaining the purpose of each folder and file. Specify how to organize the 45GB PNG image dataset, the test_list, and train_val list files within this structure. Include clear, step-by-step instructions on how to execute the code within VS Code, covering any necessary environment setup, package installations, and command-line operations. Explain the rationale behind each step to ensure a clear understanding of the process. The guide should enable a user to successfully load the dataset, train the Vision Transformer model, perform segmentation and masking to locate diseases, and visualize the results, all within the VS Code environment.

Show full message

161d ago

Chest X-ray Disease Detection and Localization with Vision Transformers

This project implements a comprehensive system for detecting and localizing diseases in chest X-rays using Vision Transformers (ViT). The system can analyze chest X-ray images to identify 14 different thoracic diseases and localize them within the image.

Dataset

The project is designed to work with the NIH Chest X-ray Dataset, which contains 112,120 X-ray images with disease labels from 30,805 unique patients. The dataset includes 14 disease categories:

Atelectasis
Cardiomegaly
Effusion
Infiltration
Mass
Nodule
Pneumonia
Pneumothorax
Consolidation
Edema
Emphysema
Fibrosis
Pleural Thickening
Hernia

Features

Vision Transformer Models: Utilizes state-of-the-art Vision Transformer architectures for both classification and segmentation tasks.
Multi-task Learning: Combines disease classification and localization in a single model.
Disease Localization: Generates heatmaps to visualize the regions associated with detected diseases.
Efficient Data Handling: Optimized for handling large datasets (45GB+) with caching and parallel processing.
Comprehensive Visualization: Tools for visualizing model predictions, attention maps, and segmentation masks.

Project Structure

data_handling.py: Functions for loading and preprocessing the dataset
vit_models.py: Vision Transformer models for classification, segmentation, and multi-task learning
training.py: Training and evaluation functions
visualization.py: Functions for visualizing model predictions and disease localization
main.py: Main script for training and evaluating models
inference.py: Script for making predictions on new images
prepare_dataset.py: Utility script for preparing the dataset

Requirements

Python 3.7+
PyTorch 1.7+
torchvision
timm (PyTorch Image Models)
numpy
pandas
matplotlib
scikit-learn
opencv-python
albumentations
einops
tqdm
wandb (optional, for experiment tracking)

Installation

pip install torch torchvision numpy pandas matplotlib scikit-learn opencv-python albumentations einops tqdm timm wandb

WorkingWorking...

Working...

Show full message

161d ago

Chest X-ray Disease Detection and Localization

Data Loading and Preprocessing

First, let's create a module to load and preprocess the X-ray images:

Chest x Ray analysis

data_loader.py

Model Architecture

Now, let's create a model for disease detection and classification:

Chest x Ray analysis

model.py

Training Module

Let's create a module for training the model:

Chest x Ray analysis

train.py

Disease Localization and Visualization

Now, let's create a module for disease localization and visualization:

Chest x Ray analysis

visualization.py

Main Script

Finally, let's create a main script to tie everything together:

Chest x Ray analysis

main.py

Inference Script

Let's also create a script for inference on new images:

Chest x Ray analysis

inference.py

Advanced Visualization with Grad-CAM

Let's implement a more advanced visualization technique using Grad-CAM:

Chest x Ray analysis

grad_cam.py

Utility Script for Data Analysis

Let's create a utility script for analyzing the dataset:

Chest x Ray analysis

data_analysis.py

README File

Let's create a README file to explain how to use the project:

Chest x Ray analysis

README.md

Usage

Data Analysis

Analyze the dataset to understand its characteristics:

python data_analysis.py --csv_file Data_Entry_2017.csv --image_dir images

Training

Train a model on the dataset:

python main.py --data_dir images --csv_file Data_Entry_2017.csv --train --model_type densenet --epochs 10 --batch_size 32

Evaluation

Evaluate a trained model:

python main.py --data_dir images --csv_file Data_Entry_2017.csv --evaluate --load_path model.pth

Visualization

Visualize disease localization:

python main.py --data_dir images --csv_file Data_Entry_2017.csv --visualize --load_path model.pth

Inference

Make predictions on new images:

python inference.py --model_path model.pth --image_path test_image.png --visualize

Or process a directory of images:

python inference.py --model_path model.pth --image_dir test_images

Model Architecture

The project implements two model architectures:

DenseNet121: A densely connected convolutional network with additional layers for disease localization.
ResNet50: A residual network with additional layers for disease localization.

Both models are pretrained on ImageNet and fine-tuned on the chest X-ray dataset.

Disease Localization

The project implements two methods for disease localization:

Feature Maps: Using the feature maps from the last convolutional layer to generate heatmaps for each disease.
Grad-CAM: Using gradient-weighted class activation mapping to visualize which regions of the image are important for the model's prediction.

References

Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. IEEE CVPR.


This comprehensive solution provides a complete framework for chest X-ray disease detection and localization. The code is modular and well-documented, making it easy to understand and extend. The project includes data loading, model training, evaluation, visualization, and inference components, as well as utilities for analyzing the dataset.

161d ago

Enhanced Chest X-ray Analysis with Vision Transformers

Data Handling Module

First, let's create a module to efficiently handle the large dataset:

Chest x Ray vit analysis

data_handling.py

Vision Transformer Models

Now, let's implement the Vision Transformer models for classification and segmentation:

Chest x Ray vit analysis

vit_models.py

Training and Evaluation Module

Now, let's create a module for training and evaluating the models:

Chest x Ray vit analysis

training.py

Visualization and Disease Localization

Let's create a module for visualizing the results and localizing diseases:

Chest x Ray vit analysis

visualization.py

Main Script

Now, let's create a main script to tie everything together:

Chest x Ray vit analysis

main.py

Inference Script

Let's create a script for inference on new images:

Chest x Ray vit analysis

inference.py

Utility Script for Data Preparation

Let's create a utility script for preparing the dataset:

Chest x Ray vit analysis

prepare_dataset.py

README File

Let's create a README file to explain how to use the project:

Chest x Ray vit analysis

README.md

Usage

Data Preparation

Prepare the NIH Chest X-ray Dataset for training:

python prepare_dataset.py --data_dir /path/to/images --output_dir ./prepared_data --csv_file Data_Entry_2017.csv --prepare_dataset --create_masks

Data Analysis

Analyze the dataset to understand its characteristics:

python main.py --data_dir ./prepared_data --train_list ./prepared_data/train.csv --test_list ./prepared_data/test.csv --analyze_dataset

Training

Train a multi-task model for disease classification and localization:

python main.py --data_dir ./prepared_data --train_list ./prepared_data/train.csv --test_list ./prepared_data/test.csv --model_type multitask --train --epochs 30 --batch_size 16 --checkpoint_dir ./checkpoints

Evaluation

Evaluate a trained model:

python main.py --data_dir ./prepared_data --test_list ./prepared_data/test.csv --model_type multitask --evaluate --load_path ./checkpoints/best_model.pth

Visualization

Visualize disease localization:

python main.py --data_dir ./prepared_data --test_list ./prepared_data/test.csv --model_type multitask --visualize --load_path ./checkpoints/best_model.pth

Inference

Make predictions on new images:

python inference.py --model_type multitask --model_path ./checkpoints/best_model.pth --image_path test_image.png --localize --output_dir ./output

Or process a directory of images:

python inference.py --model_type multitask --model_path ./checkpoints/best_model.pth --image_dir ./test_images --localize --output_dir ./output --save_csv

Model Architecture

Vision Transformer for Classification

Vision Transformer for Segmentation

The segmentation model extends the ViT architecture with a decoder that transforms the patch embeddings back into a spatial representation for pixel-level segmentation.

Multi-task Learning

The multi-task model combines classification and segmentation in a single architecture, sharing the encoder between both tasks while having separate heads for classification and segmentation.

Disease Localization

The project implements two methods for disease localization:

Attention Maps: Using the attention weights from the Vision Transformer to generate heatmaps that highlight regions the model focuses on for each disease.
Segmentation Masks: Using the segmentation model to generate pixel-level masks for each disease.

References

Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). ChestX-ray8: Hospital-scale Chest X-ray Database L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. IEEE CVPR.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR 2021.
Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., ... & Zhou, Y. (2021). TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv preprint arXiv:2102.04306.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

The NIH for providing the Chest X-ray Dataset
The authors of the Vision Transformer architecture
The PyTorch and timm communities for their excellent libraries

161d ago

Show full message

161d ago

Chest X-ray Disease Detection and Localization with Vision Transformers

Dataset

The project is designed to work with the NIH Chest X-ray Dataset, which contains 112,120 X-ray images with disease labels from 30,805 unique patients. The dataset includes 14 disease categories:

Atelectasis
Cardiomegaly
Effusion
Infiltration
Mass
Nodule
Pneumonia
Pneumothorax
Consolidation
Edema
Emphysema
Fibrosis
Pleural Thickening
Hernia

Features

Vision Transformer Models: Utilizes state-of-the-art Vision Transformer architectures for both classification and segmentation tasks.
Multi-task Learning: Combines disease classification and localization in a single model.
Disease Localization: Generates heatmaps to visualize the regions associated with detected diseases.
Efficient Data Handling: Optimized for handling large datasets (45GB+) with caching and parallel processing.
Comprehensive Visualization: Tools for visualizing model predictions, attention maps, and segmentation masks.

Project Structure

data_handling.py: Functions for loading and preprocessing the dataset
vit_models.py: Vision Transformer models for classification, segmentation, and multi-task learning
training.py: Training and evaluation functions
visualization.py: Functions for visualizing model predictions and disease localization
main.py: Main script for training and evaluating models
inference.py: Script for making predictions on new images
prepare_dataset.py: Utility script for preparing the dataset

Requirements

Python 3.7+
PyTorch 1.7+
torchvision
timm (PyTorch Image Models)
numpy
pandas
matplotlib
scikit-learn
opencv-python
albumentations
einops
tqdm
wandb (optional, for experiment tracking)

Installation

pip install torch torchvision numpy pandas matplotlib scikit-learn opencv-python albumentations einops tqdm timm wandb

WorkingWorking...

Working...