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A deep residual architecture for skin lesion segmentation

Gurram Munirathnam, Venkatesh orcid logoORCID: 0000-0002-4393-9267, Yarlapati Ganesh, Naresh, Little, Suzanne orcid logoORCID: 0000-0003-3281-3471 and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2018) A deep residual architecture for skin lesion segmentation. In: ISIC Skin Image Analysis Workshop and Challenge @ MICCAI 2018, September 20, 2018, Granada, Spain.

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.
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Uncontrolled Keywords:Skin Lesion; FCNs; Residual connection; U-Net
Subjects:Computer Science > Machine learning
Computer Science > Multimedia systems
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Copyright Information:© 2018 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 License. View License
Funders:Science Foundation Ireland (SFI) under grant number SFI/12/RC/2289
ID Code:22685
Deposited On:21 Sep 2018 11:34 by Mr Venkatesh Gurum Munirathnam . Last Modified 25 Jan 2021 14:37

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