CoDA-Few: Few Shot Domain Adaptation for Medical Image Semantic Segmentation

Abstract

Due to ethical and legal concerns related to privacy, medical image datasets are often kept private, preventing invaluable annotations from being publicly available. However, data-driven models as machine learning algorithms require large amounts of curated labeled data. This tension between ethical concerns regarding privacy and performance is one of the core limitations to the development of artificial intelligence solutions in medical imaging analysis. Aiming to mitigate this problem, we introduce a methodology based on few-shot domain adaptation capable of leveraging organ segmentation annotations from private datasets to segment previously unseen data. This strategy uses unsupervised image-to-image translation to transfer annotations from a confidential source dataset to a set of unseen public datasets. Experiments show that the proposed method achieves equivalent or better performance when compared with approaches that have access to the target data. The method's effectiveness is evaluated in segmentation studies of the heart and lungs in X-ray datasets, often reaching Jaccard values larger than 90% for novel unseen image sets.