# LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network Source: [LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:35:17.063Z Authors: Dhanesh Ramachandram, Terrance DeVries Published: 2017-03-09T17:52:28.000Z ## Abstract 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 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 add capacity to the network and (iii) state-of-art super-resolution upsampling of predictions using subpixel CNN layers. We reported a mean IOU score of 0.642 on the validation set provided by the organisers. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()