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Quick Start - AutoML

AutoML Types in AUCMEDI¤

AUCMEDI offers a CLI and Docker interface for automatic building and fast application of state-of-the-art medical image classification pipelines.

AutoML Overview of AUCMEDI

Figure: AUCMEDI AutoML Flowchart diagram of AUCMEDI AutoML showing the pipeline workflow and three AutoML modes: training for model fitting, prediction for inference of unknown images, and evaluation for performance estimation.

Dataset Setup¤

AUCMEDI AutoML expects a fixed dataset structure if run on default parameters.
The dataset structure is by default in the working directory for CLI or is mounted as volume into the container for Docker.

aucmedi.data/
├── training/                     # Required for training
│   ├── class_a/
│   │   ├── img_x.png
│   │   └── ...
│   ├── class_b/                  # Subdirectory for each class
│   │   ├── img_y.png
│   │   └── ...
│   ├── class_c/
│   │   ├── img_z.png             # Image names have to be unique
│   │   └── ...                   # between subdirectories
│   └── ...
├── test/                         # Required for prediction
│   ├── unknown_img_n.png
│   └── ...
├── model/                        # Will be created by training
├── evaluation/                   # Will be created by evaluation
└── preds.csv                     # Will be created by prediction

Basic Usage - CLI¤

This example demonstrates the basic installation and application of AUCMEDI AutoML with the CLI. The dataset have to be located in the working directory (inside of aucmedi.data/).

Install AUCMEDI via PyPI 

pip install aucmedi

Train a model and classify unknown images 

# Run training with default arguments, but a specific architecture
aucmedi training --architecture "DenseNet121"

# Run prediction with default arguments
aucmedi prediction

Basic Usage - Docker¤

This example demonstrates the basic installation and application of AUCMEDI AutoML with Docker. The dataset have to be mounted with a volume (with an absolute file path like in the example).

Install AUCMEDI via GitHub Container Registry 

docker pull ghcr.io/frankkramer-lab/aucmedi:latest

Train a model and classify unknown images 

# Run training with default arguments, but a specific architecture
docker run \
  -v /home/dominik/aucmedi.data:/data \
  --rm \
  ghcr.io/frankkramer-lab/aucmedi:latest \
  training \
  --architecture "DenseNet121"

# Run prediction with default arguments
docker run \
  -v /home/dominik/aucmedi.data:/data \
  --rm \
  ghcr.io/frankkramer-lab/aucmedi:latest \
  prediction

More Details¤

More examples can be found here: Examples - AutoML

The full documentation for AUCMEDI AutoML can be found here: AutoML - Overview