Progressive Weighted Self-Training Ensemble for Multi-Type Skin Lesion Semantic Segmentation
In this study, we propose the Progressive Weighted Self-training Ensemble (PWStE) method that reinforces efficiency of labeled data for multi-type skin lesion semantic segmentation. The generation of multi-type skin lesion labeled data is extremely expensive as it should only be performed by dermato...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9953982/ |
| _version_ | 1797976523282579456 |
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| author | Cheolwon Lee Sangwook Yoo Semin Kim Jongha Lee |
| author_facet | Cheolwon Lee Sangwook Yoo Semin Kim Jongha Lee |
| author_sort | Cheolwon Lee |
| collection | DOAJ |
| description | In this study, we propose the Progressive Weighted Self-training Ensemble (PWStE) method that reinforces efficiency of labeled data for multi-type skin lesion semantic segmentation. The generation of multi-type skin lesion labeled data is extremely expensive as it should only be performed by dermatologists due to the small pixel variations and irregularly shaped lesion characteristics. For the reason, the reality is that labeled data for skin lesion segmentation model training is absolutely insufficient. The core idea of the proposed PWStE method is to minimize the transfer of uncertainty in the training phase of general SSL by progressively using the pseudo-labeled data referenced in training. The PWStE uses procedures such as Progressive Selector, Ensemble, and Pseudo Labeler designed using conventional Semi-Supervised Learning (SSL) concepts to more accurately generate detailed features of skin lesions from unlabeled data to pseudo-labeled data. We performed ensembles using a combination of models (U-Net, FPN, LinkerNet, PSPNet) and backbones (ResNet50, EfficientNet-b3, InceptionV3, DenseNet121, SE-ResNet101, SE-ResNeXt101). Validation was performed on our Multi-Type Skin Lesion Label Database (MSLD) dataset compared to conventional SSL methods. The experiments have shown that the model trained with PWStE shows similar results to the model of trained the entire label data using the Supervised Learning (SL) method, even with 30% less label data. These results show that our proposed PWStE can increase the efficiency of the given labeled data even in the multi-type skin lesion field. |
| first_indexed | 2024-04-11T04:52:16Z |
| format | Article |
| id | doaj.art-7d1f6e1cb7d84885aebac7150a8223bc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T04:52:16Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7d1f6e1cb7d84885aebac7150a8223bc2022-12-27T00:00:26ZengIEEEIEEE Access2169-35362022-01-011013237613238310.1109/ACCESS.2022.32227889953982Progressive Weighted Self-Training Ensemble for Multi-Type Skin Lesion Semantic SegmentationCheolwon Lee0Sangwook Yoo1Semin Kim2Jongha Lee3AI Research and Development Center, Lulu Lab Inc., Seoul, South KoreaAI Research and Development Center, Lulu Lab Inc., Seoul, South KoreaAI Research and Development Center, Lulu Lab Inc., Seoul, South KoreaAI Research and Development Center, Lulu Lab Inc., Seoul, South KoreaIn this study, we propose the Progressive Weighted Self-training Ensemble (PWStE) method that reinforces efficiency of labeled data for multi-type skin lesion semantic segmentation. The generation of multi-type skin lesion labeled data is extremely expensive as it should only be performed by dermatologists due to the small pixel variations and irregularly shaped lesion characteristics. For the reason, the reality is that labeled data for skin lesion segmentation model training is absolutely insufficient. The core idea of the proposed PWStE method is to minimize the transfer of uncertainty in the training phase of general SSL by progressively using the pseudo-labeled data referenced in training. The PWStE uses procedures such as Progressive Selector, Ensemble, and Pseudo Labeler designed using conventional Semi-Supervised Learning (SSL) concepts to more accurately generate detailed features of skin lesions from unlabeled data to pseudo-labeled data. We performed ensembles using a combination of models (U-Net, FPN, LinkerNet, PSPNet) and backbones (ResNet50, EfficientNet-b3, InceptionV3, DenseNet121, SE-ResNet101, SE-ResNeXt101). Validation was performed on our Multi-Type Skin Lesion Label Database (MSLD) dataset compared to conventional SSL methods. The experiments have shown that the model trained with PWStE shows similar results to the model of trained the entire label data using the Supervised Learning (SL) method, even with 30% less label data. These results show that our proposed PWStE can increase the efficiency of the given labeled data even in the multi-type skin lesion field.https://ieeexplore.ieee.org/document/9953982/Deep learningensemblemulti-type skin lesionsemi-supervised learningsemantic segmentation |
| spellingShingle | Cheolwon Lee Sangwook Yoo Semin Kim Jongha Lee Progressive Weighted Self-Training Ensemble for Multi-Type Skin Lesion Semantic Segmentation IEEE Access Deep learning ensemble multi-type skin lesion semi-supervised learning semantic segmentation |
| title | Progressive Weighted Self-Training Ensemble for Multi-Type Skin Lesion Semantic Segmentation |
| title_full | Progressive Weighted Self-Training Ensemble for Multi-Type Skin Lesion Semantic Segmentation |
| title_fullStr | Progressive Weighted Self-Training Ensemble for Multi-Type Skin Lesion Semantic Segmentation |
| title_full_unstemmed | Progressive Weighted Self-Training Ensemble for Multi-Type Skin Lesion Semantic Segmentation |
| title_short | Progressive Weighted Self-Training Ensemble for Multi-Type Skin Lesion Semantic Segmentation |
| title_sort | progressive weighted self training ensemble for multi type skin lesion semantic segmentation |
| topic | Deep learning ensemble multi-type skin lesion semi-supervised learning semantic segmentation |
| url | https://ieeexplore.ieee.org/document/9953982/ |
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