Rui Fan
ABSTRACT
Melanoma is the leading cause of death from skin cancer, and the number is increasing every year. However, automated segmentation of melanoma remains a challenging problem due to the great variation in shape, colour and texture of melanoma. Moreover, with the development of mobile devices, achieving higher performance segmentation on embedded devices deserves further research. To address the above issues, this paper proposes a lightweight network for skin lesion segmentation with guided learning based on the attention mechanism, which not only ensures image segmentation accuracy using an efficient feature fusion module, but also effectively reduces the complexity of the model. Extensive experiments on the ISIC2017 dataset validate that EGFNet achieves very competitive results in terms of objective metrics.
- Z. Ge, S. Demyanov, R. Chakravorty, A. Bowling, R. Garnavi, Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2017, pp. 250–258.Google Scholar
- Shelhamer, Evan “Fully Convolutional Networks for Semantic Segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (2017): 640-651.Google Scholar
- Ronneberger, Olaf “U-Net: Convolutional Networks for Biomedical Image Segmentation.” MICCAI (2015).Google Scholar
- Badrinarayanan, Vijay “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (2017): 2481-2495.Google Scholar
- Xie, Yutong “A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification.” IEEE Transactions on Medical Imaging 39 (2020): 2482-2493.Google Scholar
- Qamar, Saqib “Dense Encoder-Decoder–Based Architecture for Skin Lesion Segmentation.” Cognitive Computation 13 (2021): 583-594.Google Scholar
- Paszke, Adam “ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.” ArXiv abs/1606.02147 (2016): n. pag.Google Scholar
- Wang, Yu “Lednet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation.” 2019 IEEE International Conference on Image Processing (ICIP) (2019): 1860-1864.Google Scholar
- Zhang, Xiwei “Choroidal Neovascularization Segmentation Based on 3D CNN with Cross Convolution Module.” (2021).Google Scholar
- He, Kaiming “Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.” IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (2015): 1904-1916.Google Scholar
- Zhao, Hengshuang “Pyramid Scene Parsing Network.” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017): 6230-6239.Google Scholar
- Chen, Liang-Chieh “Rethinking Atrous Convolution for Semantic Image Segmentation.” ArXiv abs/1706.05587 (2017): n. pag.Google Scholar
- Woo, Sanghyun “CBAM: Convolutional Block Attention Module.” ECCV (2018).Google Scholar
- N.C.F. Codella, D. Gutman, M.E. Celebi, , “Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)”, Proc. of International Symposium on Biomedical Imaging, 2018:168–172, 2018. DOI: 10.1109/ISBI.2018.8363547.Google ScholarCross Ref
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