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Dynamic prototypical feature representation learning framework for semi-supervised skin lesion segmentation.
- Source :
-
Neurocomputing . Oct2022, Vol. 507, p369-382. 14p. - Publication Year :
- 2022
-
Abstract
- Automated skin lesion segmentation is an essential yet challenging task for computer-aided skin disease diagnosis. One major challenge for learning-based segmentation method is the limited manually annotated dermoscopy images. Many semi-supervised methods are proposed to exploit unlabeled data by self-training with pseudo labels. However, the plain pseudo labels are less accurate and the pixel-wise features of unlabeled data are always not well formulated due to the large variations among different lesions. Aiming at producing a good segmentation embedding space in a semi-supervised manner, in this paper, we propose a novel dynamic prototypical feature representation learning framework to address these problems. Specifically, we propose a novel denoised pseudo label generation method, which effectively filters out the unreliable components in plaint pseudo labels and provides the guidance for the subsequent feature representation learning. Then, we propose a memory relation learning method to enhance the intermediate feature representation globally. Additionally, we propose a prototype-based confidence-aware contrastive learning method to learn a better local feature structure in semi-supervised training, strengthening intra-class compactness and inter-class separability. Extensive experiments on two skin lesion segmentation datasets demonstrate that our method outperforms other popular semi-supervised segmentation methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SKIN disease diagnosis
*PIXELS
Subjects
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 507
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 158748382
- Full Text :
- https://doi.org/10.1016/j.neucom.2022.08.039