  ivadomed is an open-source Python package for designing, end-to-end training,
and evaluating deep learning models applied to medical imaging data. The
package includes APIs, command-line tools, documentation, and tutorials.
ivadomed also includes pre-trained models such as spinal tumor segmentation and
vertebral labeling. Original features of ivadomed include a data loader that
can parse image metadata (e.g., acquisition parameters, image contrast,
resolution) and subject metadata (e.g., pathology, age, sex) for custom data
splitting or extra information during training and evaluation. Any dataset
following the Brain Imaging Data Structure (BIDS) convention will be compatible
with ivadomed without the need to manually organize the data, which is
typically a tedious task. Beyond the traditional deep learning methods,
ivadomed features cutting-edge architectures, such as FiLM and HeMis, as well
as various uncertainty estimation methods (aleatoric and epistemic), and losses
adapted to imbalanced classes and non-binary predictions. Each step is
conveniently configurable via a single file. At the same time, the code is
highly modular to allow addition/modification of an architecture or
pre/post-processing steps. Example applications of ivadomed include MRI object
detection, segmentation, and labeling of anatomical and pathological
structures. Overall, ivadomed enables easy and quick exploration of the latest
advances in deep learning for medical imaging applications. ivadomed's main
project page is available at https://ivadomed.org.
