The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation
Open accessAbstract
A common shortfall of supervised deep learning for medical imaging is the lack of labeled data, which is often expensive and time consuming to collect. This article presents a new semisupervised method for medical image segmentation, where the network is optimized by a weighted combination of a common supervised loss only for the labeled inputs and a regularization loss for both the labeled and unlabeled data. To utilize the unlabeled data, our method encourages consistent predictions of the network-in-training for the same input under different perturbations. With the semisupervised segmentation tasks, we introduce a transformation-consistent strategy in the self-ensembling model to enhance the regularization effect for pixel-level predictions. To further improve the regularization effects, we extend the transformation in a more generalized form including scaling and optimize the consistency loss with a teacher model, which is an averaging of the student model weights. We extensively validated the proposed semisupervised method on three typical yet challenging medical image segmentation tasks: 1) skin lesion segmentation from dermoscopy images in the International Skin Imaging Collaboration (ISIC) 2017 data set; 2) optic disk (OD) segmentation from fundus images in the Retinal Fundus Glaucoma Challenge (REFUGE) data set; and 3) liver segmentation from volumetric CT scans in the Liver Tumor Segmentation Challenge (LiTS) data set. Compared with state-of-the-art, our method shows superior performance on the challenging 2-D/3-D medical images, demonstrating the effectiveness of our semisupervised method for medical image segmentation.
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Cites
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“4) Implementation of π operation: Multi-scale ensembling is shown to be effective for image recognition [25–27].”
BackgroundMethodAlbumentations: fast and flexible image augmentations
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The Liver Tumor Segmentation Benchmark (LiTS)
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Cited by
Recent advances and clinical applications of deep learning in medical image analysis
Chen, X., Wang, X., Zhang, K., Fung, K.-M., Thai, T. C., Moore, K., Mannel, R. S., Liu, H., Zheng, B., & Qiu, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102444 · Cited by 1,080
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“Liskowski and Krawiec (2016) apply gamma correction of saturation and value of the HSV color space prior to segmenting retinal blood vessels.”
BackgroundResultInfluentialMedical image segmentation using deep learning: A survey
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Deep semantic segmentation of natural and medical images: a review
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Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency
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Metadata
- Authors
- Li, X., Yu, L., Chen, H., Fu, C.-W., Xing, L., & Heng, P.-A.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2021-02
- Volume
- 32
- Issue
- 2
- Pages
- 523-534
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W3031923721
- Cited by
- 543
- References
- 15
- FWCI
- 28.91
- Type
- journal article
- Language
- en
- Open access
- green
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- Li, X., Yu, L., Chen, H., Fu, C.-W., Xing, L., & Heng, P.-A. (2021). Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2020.2995319
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