# Project Steps: Tumor Detection and Segmentation

This document outlines the current and planned steps for developing a tumor detection and segmentation system using deep learning (MONAI, PyTorch) for MRI/CT images.

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## Current Steps

1. **Project Setup**
   - Create directory structure for data, models, notebooks, source code, and configuration.
   - Initialize Python package and configuration files.

2. **Environment Preparation**
   - Install required dependencies (see `requirements.txt`).
   - Set up VS Code and GitHub Copilot for code assistance.

3. **Data Preparation**
   - Organize MRI/CT datasets in the `data/` directory.
   - Implement data preprocessing utilities (`src/data_preprocessing.py`).

4. **Model Development**
   - Implement training script (`src/train.py`) using MONAI's UNet.
   - Define data loaders, transforms, and training loop.

5. **Evaluation and Inference**
   - Prepare scripts for model evaluation (`src/evaluate.py`) and inference (`src/inference.py`).

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## Future Steps

1. **Sensor Fusion**
   - Integrate multi-modal data (e.g., MRI + CT, or other sensors) for improved tumor detection and segmentation.

2. **Preoperative and Postoperative Reporting**
   - Develop modules to generate preop and postop reports based on model outputs.
   - Automate report generation for clinical workflows.

3. **Deep Learning Enhancements**
   - Experiment with advanced architectures (e.g., attention mechanisms, transformers).
   - Implement model ensembling and uncertainty estimation.

4. **Patient Analysis Compared to Prior Data**
   - Analyze patient scans over time and compare with prior trained data.
   - Track tumor progression/regression and provide longitudinal insights.

5. **Clinical Integration**
   - Develop interfaces for clinicians to review, annotate, and validate results.
   - Ensure compliance with medical data standards and privacy regulations.

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## Notes

- Each step should be version-controlled and documented.
- Use VS Code with Copilot for code suggestions and productivity.
- Contributions and suggestions for new features are welcome.
