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A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification
[article]
2020
arXiv
pre-print
Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse
arXiv:1903.03313v4
fatcat:cv3ldlhts5gndpxb6ttmrlc3ya