Cascaded Context Enhancement Network for Automatic Skin Lesion Segmentation

• URL: http://arxiv.org/abs/2004.08107v3
• Date: Mon, 7 Jun 2021 08:01:03 GMT
• Title: Cascaded Context Enhancement Network for Automatic Skin Lesion Segmentation
• Authors: Ruxin Wang, Shuyuan Chen, Chaojie Ji, Ye Li
• Abstract summary: We formulate a cascaded context enhancement neural network for automatic skin lesion segmentation. A new context aggregation module with a gate-based information integration approach is proposed. We evaluate our approach on four public skin dermoscopy image datasets.
• Score: 10.648218637920035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Skin lesion segmentation is an important step for automatic melanoma diagnosis. Due to the non-negligible diversity of lesions from different patients, extracting powerful context for fine-grained semantic segmentation is still challenging today. Although the deep convolutional neural network (CNNs) have made significant improvements on skin lesion segmentation, they often fail to reserve the spatial details and long-range dependencies context due to consecutive convolution striding and pooling operations inside CNNs. In this paper, we formulate a cascaded context enhancement neural network for automatic skin lesion segmentation. A new cascaded context aggregation (CCA) module with a gate-based information integration approach is proposed to sequentially and selectively aggregate original image and multi-level features from the encoder sub-network. The generated context is further utilized to guide discriminative features extraction by the designed context-guided local affinity (CGL) module. Furthermore, an auxiliary loss is added to the CCA module for refining the prediction. In our work, we evaluate our approach on four public skin dermoscopy image datasets. The proposed method achieves the Jaccard Index (JA) of 87.1%, 80.3%, 83.4%, and 86.6% on ISIC-2016, ISIC-2017, ISIC-2018, and PH2 datasets, which are higher than other state-of-the-art models respectively.

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