  While the field of medical image analysis has undergone a transformative
shift with the integration of machine learning techniques, the main challenge
of these techniques is often the scarcity of large, diverse, and well-annotated
datasets. Medical images vary in format, size, and other parameters and
therefore require extensive preprocessing and standardization, for usage in
machine learning. Addressing these challenges, we introduce the Medical Imaging
Meta-Dataset (MedIMeta), a novel multi-domain, multi-task meta-dataset.
MedIMeta contains 19 medical imaging datasets spanning 10 different domains and
encompassing 54 distinct medical tasks, all of which are standardized to the
same format and readily usable in PyTorch or other ML frameworks. We perform a
technical validation of MedIMeta, demonstrating its utility through fully
supervised and cross-domain few-shot learning baselines.
