Synchronous Multi-Model Intelligence Platform AI at the Intersection of Intelligence & Biology
Arxelos is a research-grade platform hosting three deployed AI models, each sitting at the intersection of neuroscience and deep learning:
| Model | Domain | Status |
|---|---|---|
| Brain Tumor MRI Classifier | Medical Computer Vision | 🔧 Building |
| Virtual Lesions CNN Visualizer | Neuro-AI / Interpretability | 📋 Planned |
| RAG Medical Literature Q&A | NLP / LLM | 📋 Planned |
Each model is both a research deliverable and a deployed, demo-ready artifact.
Arxelos/
├── backend/ # FastAPI application
│ ├── app/
│ │ ├── main.py # Application entrypoint
│ │ ├── config.py # Settings & environment config
│ │ ├── routers/ # API route handlers
│ │ │ ├── health.py # Health check endpoints
│ │ │ ├── tumor.py # Model 1 — Brain Tumor Classifier
│ │ │ ├── lesions.py # Model 2 — Virtual Lesions Visualizer
│ │ │ └── rag.py # Model 3 — Medical RAG Q&A
│ │ ├── models/ # Pydantic schemas & ML model loaders
│ │ ├── services/ # Business logic & inference pipelines
│ │ └── utils/ # Shared utilities
│ ├── tests/ # pytest test suite
│ └── requirements.txt # Python dependencies
├── frontend/ # Static site & demo UI
│ ├── templates/ # Jinja2 HTML templates
│ ├── static/
│ │ ├── css/ # Stylesheets
│ │ ├── js/ # Client-side scripts
│ │ └── images/ # Assets & logos
├── docker/ # Dockerfiles & compose configs
│ ├── Dockerfile # Production image
│ └── docker-compose.yml # Local dev orchestration
├── notebooks/ # Research notebooks (Neuro-AI labs)
├── docs/ # Architecture docs & write-ups
├── scripts/ # Utility scripts
├── .github/workflows/ # CI/CD pipelines
├── .env.example # Environment variable template
├── .gitignore
└── README.md
- Python 3.10+
- Docker (optional, for containerized runs)
# Clone the repo
git clone https://github.com/aryanp2107/Arxelos.git
cd Arxelos
# Create virtual environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install dependencies
pip install -r backend/requirements.txt
# Run the API server
uvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000API docs available at http://localhost:8000/docs
cd docker
docker-compose up --build- Project structure & repository setup
- Model 1: Brain Tumor MRI Classifier — FastAPI + Docker + deploy
- Model 2: Virtual Lesions CNN Visualizer — interactive ablation demos
- Model 3: RAG Medical Literature Q&A — retrieval pipeline + deploy
- Phase 2: Multimodal orchestration layer
- CI/CD via GitHub Actions
- Model monitoring & MLOps integration
Backend: FastAPI · Uvicorn · PyTorch · TensorFlow/Keras NLP: LangChain · ChromaDB/FAISS · Transformers Frontend: Jinja2 · Vanilla JS (lightweight, fast) Infra: Docker · GitHub Actions · Cloud TBD (Render / Railway / AWS) MLOps: MLflow · DVC · GitHub Actions CI/CD
Aryan Patel
aryanp2107 · patel.aryana@northeastern.edu
MIT — see LICENSE for details.