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A Deep Residual Architecture for Skin Lesion Segmentation

Publication Type: 
Refereed Conference Meeting Proceeding
In this paper, we propose an automatic approach to skin lesion region segmentation based on a deep learning architecture with multi-scale residual connections. The architecture of the proposed model is based on UNet [22] with residual connections to maximise the learning capability and performance of the network. The information lost in the encoder stages due to the max-pooling layer at each level is preserved through the multi-scale residual connections. To corroborate the efficacy of the proposed model, extensive experiments are conducted on the ISIC 2017 challenge dataset without using any external dermatologic image set. An extensive comparative analysis is presented with contemporary methodologies to highlight the promising performance of the proposed methodology.
Conference Name: 
ISIC Skin Image Analysis Workshop and Challenge @ MICCAI 2018
Stoyanov D. et al. (eds) OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis. CARE 2018, CLIP 2018, OR 2.0 2018, ISIC 2018. Lecture Notes in Computer Science, vol 11041.
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Research Group: 
Dublin City University (DCU)
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