Type

Conference Proceedings

Authors

Noel E. O'Connor
Suzanne Little
Naresh Yarlapati Ganesh
Venkatesh Gurum Munirathnam

Subjects

Computer Science

Topics
fcns residual connection machine learning deep learning multi scale skin lesion multimedia systems u net

A deep residual architecture for skin lesion segmentation (2018)

Abstract 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.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> Subject = Computer Science: Multimedia systems
Ireland -> Dublin City University -> Status = In Press
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics
Ireland -> Dublin City University -> Subject = Computer Science: Machine learning

Full list of authors on original publication

Noel E. O'Connor, Suzanne Little, Naresh Yarlapati Ganesh, Venkatesh Gurum Munirathnam

Experts in our system

1
Noel E. O'Connor
Dublin City University
Total Publications: 474
 
2
Suzanne Little
Dublin City University
Total Publications: 36
 
3
Venkatesh Gurum Munirathnam
Dublin City University
Total Publications: 3