  Neuroimage processing tasks like segmentation, reconstruction, and
registration are central to the study of neuroscience. Robust deep learning
strategies and architectures used to solve these tasks are often similar. Yet,
when presented with a new task or a dataset with different visual
characteristics, practitioners most often need to train a new model, or
fine-tune an existing one. This is a time-consuming process that poses a
substantial barrier for the thousands of neuroscientists and clinical
researchers who often lack the resources or machine-learning expertise to train
deep learning models. In practice, this leads to a lack of adoption of deep
learning, and neuroscience tools being dominated by classical frameworks.
  We introduce Neuralizer, a single model that generalizes to previously unseen
neuroimaging tasks and modalities without the need for re-training or
fine-tuning. Tasks do not have to be known a priori, and generalization happens
in a single forward pass during inference. The model can solve processing tasks
across multiple image modalities, acquisition methods, and datasets, and
generalize to tasks and modalities it has not been trained on. Our experiments
on coronal slices show that when few annotated subjects are available, our
multi-task network outperforms task-specific baselines without training on the
task.
