@conference {cRomero-Lopeza, title = {The Impact of Segmentation on the Accuracy and Sensitivity of a Melanoma Classifier based on Skin Lesion Images}, booktitle = {Annual Meeting of the Society of Imaging Informatics in Medicine (SIIM)}, year = {2017}, month = {06/2017}, publisher = {Society of Imaging Informatics for Medicine}, organization = {Society of Imaging Informatics for Medicine}, address = {Pittsburgh, PA, USA}, abstract = {

The accuracy and sensitivity of a Deep Learning based approach for a 2-class classifier for early melanoma detection\ based on skin lesion dermoscopic images increases when\ the classifier is trained with segmented inputs (i.e., images\ containing only the lesions as binary masks, without the\ surrounding context) instead of entire images.

[SIIM 2017 Annual Meeting website]

[SIIM 2017 Session where our work is presented]

The Impact of Segmentation on the Accuracy and Sensitivity of a Melanoma Classifier Based on Skin Lesion Images from Oge Marques
}, url = {http://hdl.handle.net/2117/105582}, author = {Romero-Lopez, Adria and Burdick, Jack and Xavier Gir{\'o}-i-Nieto and Marques, Oge} } @conference {cRomero-Lopez, title = {Skin Lesion Classification from Dermoscopic Images using Deep Learning}, booktitle = {The 13th IASTED International Conference on Biomedical Engineering (BioMed 2017)}, year = {2017}, month = {02/2017}, address = {Innsbruck Austria}, abstract = {

The recent emergence of deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist the human expert in making better decisions about a patient{\textquoteright}s health. In this paper we focus on the problem of skin lesion classification, particularly early melanoma detection, and present a deep-learning based approach to solve the problem of classifying a dermoscopic image containing a skin lesion as malignant or benign. \ The proposed solution is built around the VGGNet convolutional neural network architecture and uses the transfer learning paradigm. Experimental results are encouraging: on the ISIC Archive dataset, the proposed method achieves a sensitivity value of 78.66\%, which is significantly higher than the current state of the art on that dataset.

[BioMed conference 2017]

}, keywords = {Convolutional Neural Networks, deep learning, machine learning, Medical Decision Support Systems, Medical Image Analysis, Skin Lesions}, url = {http://upcommons.upc.edu/handle/2117/103386}, author = {Romero-Lopez, Adria and Burdick, Jack and Xavier Gir{\'o}-i-Nieto and Marques, Oge} }