Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation.
IEEE Trans Biomed Eng
; 70(9): 2519-2528, 2023 09.
Article
em En
| MEDLINE
| ID: mdl-37028023
ABSTRACT
OBJECTIVE:
The scarcity of high-quality annotated data is omnipresent in machine learning. Especially in biomedical segmentation applications, experts need to spend a lot of their time into annotating due to the complexity. Hence, methods to reduce such efforts are desired.METHODS:
Self-Supervised Learning (SSL) is an emerging field that increases performance when unannotated data is present. However, profound studies regarding segmentation tasks and small datasets are still absent. A comprehensive qualitative and quantitative evaluation is conducted, examining SSL's applicability with a focus on biomedical imaging. We consider various metrics and introduce multiple novel application-specific measures. All metrics and state-of-the-art methods are provided in a directly applicable software package (https//osf.io/gu2t8/).RESULTS:
We show that SSL can lead to performance improvements of up to 10%, which is especially notable for methods designed for segmentation tasks.CONCLUSION:
SSL is a sensible approach to data-efficient learning, especially for biomedical applications, where generating annotations requires much effort. Additionally, our extensive evaluation pipeline is vital since there are significant differences between the various approaches.SIGNIFICANCE:
We provide biomedical practitioners with an overview of innovative data-efficient solutions and a novel toolbox for their own application of new approaches. Our pipeline for analyzing SSL methods is provided as a ready-to-use software package.
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Base de dados:
MEDLINE
Assunto principal:
Confiabilidade dos Dados
/
Aprendizado de Máquina
Tipo de estudo:
Qualitative_research
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article