KonfAI is a flexible and extensible deep learning framework built on PyTorch, designed for fully YAML-driven configuration.
It provides a clean separation between configuration and implementation, allowing users to orchestrate entire workflows, from data loading to evaluation, with no hardcoded logic.
KonfAI natively supports multi-model training, patch-based learning, test-time augmentation, and loss scheduling, making it ideal for medical imaging research and large-scale experimentation.
KonfAI has been used in several top-performing challenge projects:
🔗 SynthRAD2025 – Task 1 •
🔗 SynthRAD2025 – Task 2 •
🔗 CURVAS PDACVI 2025 •
🔗 TrackRAD 2025 •
🔗 Panther •
🔗 CURVAS •
For more details on the design principles and scientific background, refer to the paper:
KonfAI: A Modular and Fully Configurable Framework for Deep Learning in Medical Imaging
- 🔀 Full training/prediction/evaluation orchestration via YAML configuration files
- 🧩 Modular plugin-like structure (transforms, augmentations, models, losses, schedulers)
- 🔄 Dynamic criterion scheduling per head / target
- 🧠 Multi-branch / multi-output model support
- 🖥️ Cluster-ready
- 📈 TensorBoard and custom logging support
Install KonfAI from PyPI:
pip install konfai
This will install the command-line tools:
konfai --help
konfai-cluster --help
Clone the repository and install:
git clone https://github.com/vboussot/KonfAI.git
cd KonfAI
pip install -e .
konfai TRAIN --gpu 0
konfai PREDICTION --gpu 0
konfai EVALUATIONA KonfAI App is a self-contained workflow package built with KonfAI.
It defines how a model is executed, how outputs are generated, and how optional evaluation or uncertainty workflows are performed.
Several ready-to-use KonfAI Apps are available directly in the repository under the apps/ directory.
They can be executed identically from:
| Interface | Command |
|---|---|
| 🖥️ CLI | konfai-apps infer / eval / uncertainty / pipeline app name |
| 🧠 3D Slicer | Via SlicerKonfAI GUI https://github.com/vboussot/SlicerKonfAI |
| 🐍 Python API | Via konfai.app.KonfAIApp |
my_konfai_app/
├── app.json # Metadata for UI + behaviors
├── Prediction.yml # Inference workflow (required)
├── Evaluation.yml # Evaluation workflow (optional)
├── Uncertainty.yml # Uncertainty workflow (optional)
└── checkpoint.pt # Trained model (single or ensemble)
Example app.json:
{
"display_name": "Lung Lobe Segmentation",
"short_description": "Segmentation of lung lobes on CBCT scans.",
"description": "This App synthesizes CT-like contrast from CBCT then segments lung lobes.",
"tta": 4,
"mc_dropout": 0
}Inference:
konfai-apps infer my_app -i input.mha -o ./Predictions --tta 4Evaluation:
konfai-apps eval my_app -i input/ --gt labels/Uncertainty:
konfai-apps uncertainty my_app -i input.mhaPipeline (inference → evaluation → uncertainty):
konfai-apps pipeline my_app -i input.mha --gt gt.mha -uncertaintyFine-tuning:
konfai-apps fine-tune my_app name -d ./Dataset --epochs 20More detailed documentation and usage examples for each app are available in the corresponding subdirectories of the apps/ folder.
The official KonfAI documentation is in progress and will be released soon.
We are actively developing KonfAI-MCP, an extension of the framework enabling language-driven deep learning experimentation. Through the Model Context Protocol (MCP), KonfAI will serve as the deterministic and transparent execution layer for agentic LLMs, allowing large language models to specify, launch, and refine deep learning experiments directly through natural language.
Imagine instructing an AI to: “Train a model for lung tumor segmentation from this dataset and optimize the Dice score.” KonfAI-MCP aims to turn such instructions into reproducible, verifiable experiments.
This represents the next stage of AI-assisted scientific research, where language becomes a medium of empirical discovery.