Data scarcity is one of the major limiting factors preventing application of powerful machine learning algorithms to many medical applications beyond a handful of big public datasets. Cross-Domain Few-shot Learning (CD-FSL) offers the potential to exploit similarities between different medical image analysis datasets and leverage shared knowledge to learn previously unseen tasks more efficiently. However, CD-FSL is underexplored in medical image analysis. With the L2L challenge we want to encourage the medical image analysis and machine learning communities to explore the potential of CD-FSL approaches in the promising application domain of medical image analysis, and to develop algorithms that are robust to the extremely high task and data diversity encountered in this domain.
The L2L Challenge is an official MICCAI 2023 challenge, and the winners will be announced at the meeting Vancouver. The International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) is the top conference in the domain of medical image analysis.
Participants develop an algorithm that learns to learn new tasks data-efficiently by using the provided MIMeta dataset of datasets. The algorithm then gets evaluated for its ability to learn a set of private, previously unseen, test tasks derived from a set of private datasets. See more details in the Challenge Mechanics section.
The winning team will receive a 1000 CA$ cash prize sponsored by ImFusion!
We provide an L2L example repo with a minimal yet functional submission. Real submissions will likely be more complex, but we hope to provide a suitable starting point for participants to work from. The documentation of the repository also contains the basic requirements for submitted containers.
We release the MIMeta Dataset, a novel meta dataset comprised of 17 publicly available datasets containing a total of 28 tasks. We additionally prepared a private set of tasks derived from different datasets which will be used for validation and final testing of the submissions. All datasets included in the MIMeta dataset have been previously published under a creative commons licence. All datasets preprocessed and can be directly accessed using the data loaders provided in the The MIMeta python package (see below).
The dataset bears similarity to, and has partial overlap with, the Medical MNIST dataset. However, we go beyond Medical MNIST in the amount and diversity of tasks included in our dataset. Moreover, all images in MIMeta are standardized to an image size of 224x224 pixels which allows a more clinically meaningful analysis of the images.
Beyond the L2L challenge, our aim is to provide a valuable resource for quickly benchmarking algorithms on a wide range of medical tasks with standardised data splits.
In addition to the dataset, we also release two python packages:
An overview of the submission and evaluation procedure are shown in the figure above. For a more detailed explanation checkout our Rules page.