> An open-source multi-agent system for automated end-to-end development of radiomics and deep learning models for medical imaging  --- ## 🚀 About mAIstro **mAIstro** is an autonomous, open-source multi-agent system designed to orchestrate the full pipeline of medical imaging AI development - from **exploratory data analysis (EDA)** and **radiomics feature extraction** to **training and deploying deep learning models**. Built around a team of specialized agents, mAIstro enables researchers and clinicians to interact with complex AI workflows **using natural language prompts** - no coding required. 🌐 **LLM-Agnostic Design**: mAIstro can operate with both open-source and commercial LLMs (e.g., GPT-4, Claude, DeepSeek, LLaMA, Qwen), providing flexibility across environments. ⭐ If you find mAIstro useful, please consider starring the repository to support the project and help others discover it! --- ## ✨ Key Features - 🔎 Autonomous **Exploratory Data Analysis (EDA)** - 🧬 **Radiomics feature extraction** (for CT, MRI, and multi-parametric imaging) - ⚙️ **nnU-Net Agent** for segmentation model development and implementation - ⚙️ **TotalSegmentator Agent** for full-body and organ-specific automatic segmentation - 🩻 **Image Classification Agent** (ResNet, VGG16, InceptionV3 architectures) - 📊 **Feature Importance and Feature Selection** - 📈 **Tabular data Classification and Regression Agents** - 🛠️ Modular tool-based architecture for extensibility - 🧾 Integrated in a single user-friendly Jupyter Notebook --- ## 📢 Major Update: The Easiest Way to Use mAIstro Is Now Through ReCo! The easiest way to access and work with the **latest and most up-to-date capabilities of mAIstro** is now through **ReCo (Research Cosmos)**, our self-configuring and self-extending agentic application for biomedical research. ReCo bundles mAIstro's capabilities and exposes them to the ReCo agent through dedicated **Model Context Protocol (MCP) servers**. ### Just download ReCo and ask: > **"This is the first launch of the app, set it up."** That's it! ReCo will automatically set up the required **Conda environments, install the necessary dependencies, and configure the MCP servers** that provide mAIstro's capabilities. You can then select which capabilities you want to expose to the agent and start working with ReCo — from **data preprocessing and statistical analysis to radiomics, classification, regression, medical image segmentation, model development, evaluation, deployment, and more.** 👉 **Download ReCo:** https://github.com/eltzanis/ReCo --- ## mAIstro Desktop App 🚀 **v1.0.4r released** (04/02/2026) - mAIstro Desktop now available on **Windows and Linux**. ✨ Includes enhanced capabilities and performance improvements. 🧠 The latest versions of mAIstro tools are embedded in the mAIstro Desktop App. Experience the power of mAIstro - your intelligent desktop assistant for building complete AI pipelines in medical imaging. From radiomic feature extraction to the development, evaluation, and deployment of segmentation, classification, and regression AI models, everything happens right on your desktop by simply chatting with mAIstro. 👉 [Learn more and get download & installation instructions](./mAIstro_Desktop_app.md)

--- ## ⚙️ Using mAIstro with Docker - Setup and Run Instructions 👉 [Instructions to set up Docker and run mAIstro](./install_mAIstro_env.md) --- ## 🔗 Run mAIstro instantly on Google Colab Want to try mAIstro without setting up anything locally? You can now run the full framework interactively on Google Colab 👉 [**Launch mAIstro on Colab**](https://colab.research.google.com/drive/1aXNwh9hT9txLIiPetAtIed0_lYiCE3Wv?usp=sharing)

What’s included: All necessary requirements are automatically installed The full mAIstro_workspace folder is downloaded, including: ✅Experiment data ✅Predefined folders and structure ✅Ready-to-run example prompts pointing to the correct locations 🔐 What you need to do: The only manual step is to provide your API key for the LLM of your choice (e.g., OpenAI, Claude, DeepSeek, etc.). A pre-configured cell is provided with options for multiple LLM providers – just paste your key and you're ready to go! This makes it easy to explore and test the full functionality of mAIstro on any device, using just your browser. ### 📹 Tutorial Video - mAIstro on Colab [Watch the tutorial on Google Drive](https://drive.google.com/file/d/1EE4cgs0QOk4S_yT6aKBtHO8Bn8lvhn6A/view?usp=drive_link) --- ## 📚 Tools Documentation The following tools are part of the mAIstro multi-agentic system. Each tool is described in detail in its own documentation file: 1. [Radiomics Tool](docs/pyradiomics_tool_doc.md) 2. [Exploratory Data Analysis Tool](docs/eda_tool_doc.md) 3. [Feature Selection Tool](docs/feature_selection_tool_doc.md) 4. [nnUNet Training Tool](docs/nnUNet_training_tool_doc.md) 5. [nnUNet Inference Tool](docs/nnUNet_inference_tool_doc.md) 6. [TotalSegmentator Tool](docs/TotalSegmentator_tool.doc.md) 7. [PyCaret Classification Training Tool](docs/pycaret_classification_training_tool_doc.md) 8. [PyCaret Classification Inference Tool](docs/pycaret_classification_inference_tool_doc.md) 9. [PyCaret Regression Training Tool](docs/pycaret_regression_training_tool_doc.md) 10. [PyCaret Regression Inference Tool](docs/pycaret_regression_inference_tool_doc.md) 11. [Medical Image Classification Tools](docs/medical_image_classification_tools_doc.md) --- ## 📄 License This project is licensed under the **Apache License 2.0**. You are free to use, modify, and distribute this software under the terms of the license. --- ### 📚 Cite this work If you use **mAIstro** in your research, please cite: >Eleftherios Tzanis, Michail E. Klontzas (2025). mAIstro: An open-source multi-agent system for automated end-to-end development of radiomics and deep learning models for medical imaging. European Journal of Radiology Artificial Intelligence, 4, 100044. [https://doi.org/10.1016/j.ejrai.2025.100044](https://doi.org/10.1016/j.ejrai.2025.100044) ---