Medical Image Segmentation with Multimodal Learning

Technical Documentation

Data Efficiency Analysis: Maintained 83% performance with 90% of the data Effective even in low-data scenarios Robustness Analysis: Handles anatomical variability Adapts to complex structures

1. Implementation Overview

1.1 Core Technologies

• Deep Learning Framework: PyTorch 2.0.1
• Programming Language: Python 3.9
• Primary Libraries:
  • MONAI (Medical Open Network for AI)
  • Transformers (Hugging Face)
  • Llama-2 (Meta AI)
  • TensorBoardX (for visualization)

1.2 System Requirements

• Operating System: Linux (Ubuntu recommended) / Windows with WSL

• GPU: NVIDIA GPU with CUDA support (45GB+ VRAM recommended)

• RAM: 64GB+ recommended

• Storage: 100GB+ free space

2. Installation Guide

2.1 System Dependencies

# For Ubuntu/Debian
sudo apt-get update
sudo apt-get install python3.9 python3.9-distutils git-lfs

2.2 Python Environment Setup

# Install pip for Python 3.9
curl -sS https://bootstrap.pypa.io/get-pip.py | python3.9

# Create and activate virtual environment (recommended)
python3.9 -m venv venv
source venv/bin/activate  # Linux
# or
.\venv\Scripts\activate  # Windows

2.3 Package Installation

# Install PyTorch with CUDA support
pip install torch==2.0.1 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

# Install project dependencies
pip install -r requirements.txt

3. Project Structure

project/
├── main.py              # Entry point for training and inference
├── trainer.py           # Training and evaluation logic
├── model/              
│   ├── llama2/         # Llama-2 model integration
│   └── segmentation/   # Segmentation model architecture
├── utils/              
│   ├── data_utils.py   # Data processing utilities
│   └── metrics.py      # Evaluation metrics
├── dataset/            
│   └── external1/      # Dataset storage
├── optimizers/         # Custom optimizer implementations
├── ckpt/               # Model checkpoints
└── requirements.txt    # Project dependencies

4. Dataset Preparation

4.1 Data Format

• Input: 3D medical images in NPZ format
• Labels: Segmentation masks in NPZ format
• Text Reports: Excel/CSV format

4.2 Directory Structure

dataset/
└── external1/
    └── [case_id]/
        ├── data.npz    # 3D medical image
        └── label.npz   # Segmentation mask

5. Model Architecture

5.1 Components

1. Image Encoder: 3D UNet-based architecture
2. Text Encoder: Llama-2 7B model
3. Multimodal Fusion: Custom attention mechanism

5.2 Key Features

• 3D medical image processing
• Text report integration
• Multi-scale feature extraction
• Attention-based fusion

6. Training Process

6.1 Training Command

python main.py \
    --pretrained_dir ./ckpt/multimodal \
    --context True \
    --n_prompts 2 \
    --context_length 8 \
    --batch_size 1 \
    --roi_x 64 \
    --roi_y 352 \
    --roi_z 352

6.2 Training Parameters

• Batch Size: 1 (adjustable based on GPU memory)
• Learning Rate: 1e-4
• Optimizer: AdamW
• Loss Function: DiceCELoss
• Mixed Precision: Enabled

7. Inference

7.1 Inference Command

python main.py \
    --pretrained_dir ./ckpt/multimodal \
    --context True \
    --n_prompts 2 \
    --context_length 8 \
    --test_mode 2

7.2 Output Format

• Segmentation masks in NIfTI format
• Evaluation metrics in CSV format
• TensorBoard logs for visualization

8. Performance Considerations

8.1 Memory Requirements

• GPU Memory: 45GB+ recommended
• RAM: 64GB+ recommended
• Storage: 100GB+ for dataset and models

8.2 Optimization Tips

1. Adjust batch size based on available GPU memory
2. Use gradient accumulation for larger effective batch sizes
3. Enable mixed precision training
4. Utilize data prefetching

9. Troubleshooting

9.1 Common Issues

1. CUDA Out of Memory

   • Reduce batch size
   • Enable gradient checkpointing
   • Use mixed precision training

2. Installation Errors

   • Ensure correct Python version (3.9)
   • Install CUDA toolkit matching PyTorch version
   • Use virtual environment

3. Data Loading Issues

   • Verify NPZ file format
   • Check file permissions
   • Validate data directory structure