Semi-supervised domain adaptation incorporating three-way decision for multi-view echocardiographic sequence segmentation
Applied Soft Computing, ISSN: 1568-4946, Vol: 155, Page: 111449
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Multi-view echocardiographic sequence segmentation is essential for the diagnosis of cardiac diseases in clinical practice. However, the variation in cardiac structures in different views and the lack of manual annotations make it challenging to establish a generalized segmentation model. In this paper, we propose a Bidirectional semi-supervised domain adaptation (BSDA) method based on the three-way decision to learn a generalized segmentation model for different views. Specifically, the two-branch structure of BSDA regards echocardiographic sequence data of different views as different domains. The source-pretrained model first roughly predicted the segmentation results for the target domain. Then, BSDA provides the segmentation model with reliable probabilistic supervision and feature-based pseudo-labels to make secondary decisions. Besides, the proposed stage-wise training strategy can better cope with the varied appearance of the cardiac structures in echocardiographic sequence. We evaluate our BSDA on three publicly available datasets, corroborating the superiority of BSDA to segment cardiac structures of multi-view echocardiographic sequences.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1568494624002230; http://dx.doi.org/10.1016/j.asoc.2024.111449; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85187788405&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1568494624002230; https://dx.doi.org/10.1016/j.asoc.2024.111449
Elsevier BV
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