| # Intracranial Hemorrhage Detection |
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| AI-assisted screening system for intracranial hemorrhage (ICH) from head CT (DICOM) images. |
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| This project provides a Flask web interface for: |
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| - uploading single or batch DICOM scans, |
| - running model inference, |
| - viewing Grad-CAM visualizations, |
| - browsing past reports and logs, |
| - reviewing calibration and evaluation summaries. |
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| ## Project Overview |
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| Intracranial hemorrhage is a time-critical emergency finding in neuroimaging. This repository focuses on a practical screening workflow with explainability and structured report output. |
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| The system is built for decision support and triage assistance, not standalone diagnosis. |
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| ## Model and Artifacts |
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| Model weights and related inference artifacts are hosted on Hugging Face: |
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| - [Hugging Face Model Repository](https://huggingface.co/HarshCode/eff_b4_brain) |
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| ## Detailed Performance Report |
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| Detailed performance and B4-specific analysis are documented separately in: |
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| - [B4_Performance_Report.md](B4_Performance_Report.md) |
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| ## Repository Structure |
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| - `app.py`: Flask application entry point |
| - `run_interface.py`: adapter layer between app and inference implementation |
| - `download_imp/`: inference code and local artifact layout |
| - `templates/`: HTML templates (Jinja2) |
| - `static/`: styles and static assets |
| - `docs/`: GitHub Pages content |
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| ## Requirements |
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| - Python 3.10+ (3.12 works) |
| - pip |
| - virtual environment (recommended) |
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| Install dependencies: |
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| ```bash |
| pip install -r requirements.txt |
| ``` |
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| ## Environment Setup |
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| Create local environment file from template: |
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| ```bash |
| cp .env.example .env |
| ``` |
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| Important variables in `.env`: |
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| - `ICH_APP_DEBUG`: run Flask in debug mode (`1` or `0`) |
| - `ICH_APP_PORT`: app port (default `7860`) |
| - `ICH_SECRET_KEY`: Flask secret key |
| - `ICH_MAX_UPLOAD_MB`: max upload size in MB |
| - `ICH_FOLD_SELECTION`: `ensemble`, `best`, or fold id (`0` to `4`) |
| - `ICH_LOCAL_MODE`: enables local directory scanning mode |
| - `ICH_LOG_LEVEL`: `DEBUG`, `INFO`, `WARNING`, `ERROR` |
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| ## Run the Application |
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| ```bash |
| python app.py |
| ``` |
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| Open in browser: |
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| ```text |
| http://127.0.0.1:7860 |
| ``` |
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| ## Basic Usage |
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| 1. Go to the upload page. |
| 2. Upload one `.dcm`, multiple `.dcm` files, or batch input. |
| 3. Wait for inference and report generation. |
| 4. Review: |
| - screening outcome, |
| - calibrated probability, |
| - confidence band, |
| - triage action, |
| - Grad-CAM overlay. |
| 5. Use Reports / Logs / Evaluation pages for history and analysis. |
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| ## Notes |
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| - Keep heavy model binaries out of GitHub (managed via `.gitignore`). |
| - Generated report outputs are created during runtime. |
| - If required artifacts are missing locally, fetch them from the Hugging Face repository linked above. |
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| ## Disclaimer |
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| This system is an AI-assisted screening and decision-support tool. |
| It does **not** provide a medical diagnosis and must be used with qualified clinical review. |
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