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• W3193256763 endingPage "5172" @default.
• W3193256763 startingPage "5172" @default.
• W3193256763 abstract "Considerable research and surveys indicate that skin lesions are an early symptom of skin cancer. Segmentation of skin lesions is still a hot research topic. Dermatological datasets in skin lesion segmentation tasks generated a large number of parameters when data augmented, limiting the application of smart assisted medicine in real life. Hence, this paper proposes an effective feedback attention network (FAC-Net). The network is equipped with the feedback fusion block (FFB) and the attention mechanism block (AMB), through the combination of these two modules, we can obtain richer and more specific feature mapping without data enhancement. Numerous experimental tests were given by us on public datasets (ISIC2018, ISBI2017, ISBI2016), and a good deal of metrics like the Jaccard index (JA) and Dice coefficient (DC) were used to evaluate the results of segmentation. On the ISIC2018 dataset, we obtained results for DC equal to 91.19% and JA equal to 83.99%, compared with the based network. The results of these two main metrics were improved by more than 1%. In addition, the metrics were also improved in the other two datasets. It can be demonstrated through experiments that without any enhancements of the datasets, our lightweight model can achieve better segmentation performance than most deep learning architectures." @default.
• W3193256763 created "2021-08-16" @default.
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• W3193256763 date "2021-07-30" @default.
• W3193256763 modified "2023-09-26" @default.
• W3193256763 title "FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation" @default.
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• W3193256763 doi "https://doi.org/10.3390/s21155172" @default.
• W3193256763 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8347551" @default.
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• W3193256763 hasPublicationYear "2021" @default.
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