arXiv:1703.03372 [cs.CV]AbstractReferencesReviewsResources
LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network
Dhanesh Ramachandram, Terrance DeVries
Published 2017-03-09Version 1
We present a method for skin lesion segmentation for the ISIC 2017 Skin Lesion Segmentation Challenge. Our approach is based on a Fully Convolutional Network architecture which is trained end to end, from scratch, on a limited dataset. Our semantic segmentation architecture utilizes several recent innovations in particularly in the combined use of (i) use of \emph{atrous} convolutions to increase the effective field of view of the network's receptive field without increasing the number of parameters, (ii) the use of network-in-network $1\times1$ convolution layers to increase network capacity without incereasing the number of parameters and (iii) state-of-art super-resolution upsampling of predictions using subpixel CNN layers for accurate and efficient upsampling of predictions. We achieved a IOU score of 0.642 on the validation set provided by the organisers.