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Deep Learning for Molecular and Genomic Characterization of Lung Cancer in Never-Smokers Using Hematoxylin and Eosin-Stained Images

Mutation_AI is a deep learning project for mutation analysis using Convolutional Neural Networks (CNNs) with two main modules:

  • Multilabel_CNN: For multi-label classification tasks.
  • Binary_CNN: For binary classification tasks.

Both modules implement a custom ResNet-50-like architecture using TensorFlow/Keras.

Deep Learning for Molecular and Genomic Characterization of Lung Cancer in Never-Smokers Using Hematoxylin and Eosin-Stained Images

Table of Contents

Installation

  1. Clone the repository

    git clone https://github.com/monjoybme/Mutation_AI.git
cd Mutation_AI

  2. Install dependencies

    It is recommended to use a Python virtual environment.

    python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

Project Structure

Mutation_AI/
│
├── Multilabel_CNN/
│   ├── model.py         # Custom ResNet-50 model for multilabel classification
│   ├── main.py          # Training/Inference script (if available)
│   └── ...              # Additional utilities and scripts
│
├── Binary_CNN/
│   ├── model.py         # Custom ResNet-50 model for binary classification
│   ├── main.py          # Training/Inference script (if available)
│   └── ...              # Additional utilities and scripts
│
├── requirements.txt
└── README.md

Usage

Multilabel CNN

  1. Prepare your dataset as described in the Data Structure section.
  2. Configure parameters in Multilabel_CNN/main.py (if available).
  3. Run the training script: 
    python Multilabel_CNN/main.py

Binary CNN

  1. Prepare your dataset as described in the Data Structure section.
  2. Configure parameters in Binary_CNN/main.py (if available).
  3. Run the training script: 
    python Binary_CNN/main.py

Data Structure

  • The models expect input data in a format compatible with TensorFlow/Keras.

  • Typical directory structure for image data:

    data/
  train/
    class_1/
      img001.png
      img002.png
      ...
    class_2/
      ...
  val/
    class_1/
    class_2/

  • For multilabel tasks, a CSV file with file paths and corresponding label vectors is often used.

  • For binary tasks, two folders (e.g., positive/ and negative/) or a similar structure.

Note: Adjust data loading utilities as needed for your specific data organization.

Model Architecture

Both Multilabel_CNN/model.py and Binary_CNN/model.py implement a custom ResNet-50-like architecture using TensorFlow/Keras:

  • Initial convolutional and pooling layers
  • Multiple custom residual blocks (with optional shortcuts)
  • Global Average Pooling
  • Dense layers with dropout
  • Output layer:
    • sigmoid activation for multilabel and binary classification

The output layer's size is determined by the number of classes (multi-label) or 1 (binary).

Requirements

See requirements.txt for full details.

Main dependencies:

  • tensorflow>=2.0
  • numpy
  • pandas
  • scikit-learn
  • matplotlib (optional, for plotting)
  • tqdm (optional, for progress bars)

Contributing

Contributions are welcome! Please open issues or pull requests for improvements or bug fixes.

License

This project is licensed under the MIT License.