ArticleJournal of digital imaging2023
Skin Lesion Segmentation in Dermoscopic Images with Noisy Data.
Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- HyperFusionNet combines vision transformer for early melanoma detection and precise lesion segmentation.Scientific reports · 2025Article
- A deep learning-based dual-branch framework for automated skin lesion segmentation and classification via dermoscopic Images.Scientific reports · 2025Article
- Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging.Bioengineering (Basel, Switzerland) · 2025Article
- LAMA: Lesion-Aware Mixup Augmentation for Skin Lesion Segmentation.Journal of imaging informatics in medicine · 2024Article
- MADR-Net: multi-level attention dilated residual neural network for segmentation of medical images.Scientific reports · 2024Article
- Hybrid Topological Data Analysis and Deep Learning for Basal Cell Carcinoma Diagnosis.Journal of imaging informatics in medicine · 2024Article
- AFCF-Net: A novel U-Net based asymmetric feature calibration and fusion network for skin lesion image segmentation.PloS one · 2024Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose a deep learning approach to segment the skin lesion in dermoscopic images. The proposed network architecture uses a pretrained EfficientNet model in the encoder and squeeze-and-excitation residual structures in the decoder. We applied this approach on the publicly available International Skin Imaging Collaboration (ISIC) 2017 Challenge skin lesion segmentation dataset. This benchmark dataset has been widely used in previous studies. We observed many inaccurate or noisy ground truth labels. To reduce noisy data, we manually sorted all ground truth labels into three categories - good, mildly noisy, and noisy labels. Furthermore, we investigated the effect of such noisy labels in training and test sets. Our test results show that the proposed method achieved Jaccard scores of 0.807 on the official ISIC 2017 test set and 0.832 on the curated ISIC 2017 test set, exhibiting better performance than previously reported methods. Furthermore, the experimental results showed that the noisy labels in the training set did not lower the segmentation performance. However, the noisy labels in the test set adversely affected the evaluation scores. We recommend that the noisy labels should be avoided in the test set in future studies for accurate evaluation of the segmentation algorithms.
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Registered trials
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