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- W2897538002 abstract "The computer-aided diagnosis (CAD) systems can highly improve the reliability and efficiency of melanoma recognition. As a crucial step of CAD, skin lesion segmentation has the unsatisfactory accuracy in existing methods due to large variability in lesion appearance and artifacts. In this work, we propose a framework employing multi-stage UNets (MS-UNet) in the auto-context scheme to segment skin lesion accurately end-to-end. We apply two approaches to boost the performance of MS-UNet. First, UNet is coupled with a context information fusion structure (CIFS) to integrate the low-level and context information in the multi-scale feature space. Second, to alleviate the gradient vanishing problem, we use deep supervision mechanism through supervising MS-UNet by minimizing a weighted Jaccard distance loss function. Four out of five commonly used performance metrics, including Jaccard index and Dice coefficient, show that our approach outperforms the state-ofthe-art deep learning based methods on the ISBI 2016 Skin Lesion Challenge dataset." @default.
- W2897538002 created "2018-10-26" @default.
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- W2897538002 date "2018-10-16" @default.
- W2897538002 modified "2023-10-14" @default.
- W2897538002 title "A Multi-stage Framework with Context Information Fusion Structure for Skin Lesion Segmentation" @default.
- W2897538002 doi "https://doi.org/10.48550/arxiv.1810.07075" @default.
- W2897538002 hasPublicationYear "2018" @default.
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