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

Arthur B. A. Pinto, Jefersson Santos, Jefersson Santos, Hugo Oliveira, Alexei Machado, Alexei Machado

2023

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.

Paper Citation

in Harvard Style

B. A. Pinto A., Santos J., Oliveira H. and Machado A. (2023). CoDA-Few: Few Shot Domain Adaptation for Medical Image Semantic Segmentation. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 715-726. DOI: 10.5220/0011616800003417

in Bibtex Style

@conference{visapp23,
author={Arthur B. A. Pinto and Jefersson Santos and Hugo Oliveira and Alexei Machado},
title={CoDA-Few: Few Shot Domain Adaptation for Medical Image Semantic Segmentation},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={715-726},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011616800003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - CoDA-Few: Few Shot Domain Adaptation for Medical Image Semantic Segmentation
SN - 978-989-758-634-7
AU - B. A. Pinto A.
AU - Santos J.
AU - Oliveira H.
AU - Machado A.
PY - 2023
SP - 715
EP - 726
DO - 10.5220/0011616800003417
PB - SciTePress