Purpose

The purpose of the ivadomed project is to:

• Provide researchers with an open-source framework for training deep learning models for applications in medical imaging;

• Provide ready-to-use Pre-trained models trained on multi-center data.

Comparison with other projects

We acknowledge the existence of projects with similar purposes. The table below compares some features across some of the existing projects. This table was mostly based on the existing documentation for each project. We understand that the field is rapidly evolving, and that this table might reflect the reality. If you notice inconsistencies, please let us know by opening an issue.

Feature	ivadomed	monai	delira	MIC-DKFZ	ANTsPyNet	DLTK	MIScnn	niftytorch	DeepNeuro	DeepReg
BIDS (1)	✔	✗	✗	✗	✗	✗	✗	✔	✗	✗
DL base library	PyTorch	PyTorch	PyTorch, TF	PyTorch	TF/Keras	TF	TF/Keras	PyTorch	TF/Keras	TF
Task (2)	Class, Seg, Detect	Seg, Class	Class, Gen, Seg	Detect	Class, Seg, Clust, Reg	Class, Seg, Reg	Seg	Class, Seg	Seg	Reg
Data dimension	2D, 3D	2D, 3D	2D, 3D	2D, 3D	2D, 3D	3D	2D, 3D	3D	2D, 3D	2D, 3D
Multichannel	✔	✔	✗	✗	✗	✗	✗	✗	✗	✔
Multilabel	✔	✔	✗	✗	✔	✔	✔	✗	✗	✔
Uncertainty	✔	✗	✗	✗	✗	✗	✗	✗	✗	✗
Transfer Learning	✔	✗	✔	✗	✗	✔	✗	✗	✗	✗
Pre-processing tools	✔	✔	✗	✗	✗	✗	✔	✗	✔	✔
Post-processing tools	✔	✗	✗	✗	✗	✗	✔	✗	✔	✔
User case examples	✔	✔	✔	✗	✔	✔	✗	✔	✔	✔
Multi-GPU data parallelism	✗	✔	✔	✗	✗	✗	✔	✔	✗	✗
Model evaluation	✔	✔	✗	✔	✗	✗	✔	✗	✗	✔
Input region of interest	✔	✔	✗	✔	✗	✗	✗	✔	✗	✔
Missing modality	✔	✗	✗	✗	✗	✗	✗	✗	✗	✔
Models comparison	✔	✗	✗	✗	✗	✗	✗	✗	✗	✔
Hyperparam optimisation	✔	✗	✗	✗	✗	✗	✗	✔	✗	✔
Multi-center models	✔	✗	✗	✗	✗	✗	✗	✗	✔	✗

(1): “BIDS” stands for the Brain Imaging Data Structure, which is a convention initiated by the neuroimaging community to organize datasets (filenames, metadata, etc.). This facilitates the sharing of datasets and minimizes the burden of organizing datasets for training.

(2): Class: Classification | Seg: Segmentation | Detect: Detection | Gen: Generation | Clust: Clustering | Reg: Registration