Label-efficient Generalizable Deep Learning for Medical Image Segmentation

Page view(s)
12
Checked on Feb 19, 2024
Label-efficient Generalizable Deep Learning for Medical Image Segmentation
Title:
Label-efficient Generalizable Deep Learning for Medical Image Segmentation
Journal Title:
International Conference on AI in Medicine (iAIM)
DOI:
Publication URL:
Authors:
Publication Date:
05 August 2023
Citation:
N.A.
Abstract:
While deep learning hitherto has achieved considerable success in medical image segmentation, existing methods suffer from significant performance degradation under complex real-world clinical scenarios due to two main factors: (1) Label scarcity: reliance on large-scale well-annotated datasets, and (2) Domain shift: failure to apply well-trained models trained on old labeled datasets to new unlabeled datasets with different data distributions. In this paper, we introduce our recent works on labelefficient unsupervised domain adaptation to address both label scarcity and domain shift for crossdomain medical image analysis. We explore and advance various label-efficient learning paradigms with applications to medical image segmentation for improving model generalization and efficiency.
License type:
Publisher Copyright
Funding Info:
This research is supported by core funding from: Institute for Infocomm Research, Science and Engineering Research Council
Grant Reference no. : N.A.
Description:
ISSN:
N.A.
Files uploaded:

File Size Format Action
iaim-2023-ziyuanzhao-1.pdf 224.20 KB PDF Open