@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} } @mastersthesis {xRomero-Lopez, title = {Skin Lesion Detection from Dermoscopic Images using Convolutional Neural Networks}, year = {2017}, abstract = {

Advisors: Oge Marques (Florida Atlantic University) and Xavier Giro-i-Nieto (UPC)

The recent emergence of machine learning and deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist physicians in making better decisions about a patient{\textquoteright}s health. In particular, skin imaging is a field where these new methods can be applied with a high rate of success.\ 

This thesis focuses on the problem of automatic skin lesion detection, \ particularly on melanoma detection, by applying semantic segmentation and classification from dermoscopic images using a deep learning based approach.\ For the first problem, a U-Net convolutional neural network architecture is applied for an accurate extraction of the lesion region.\ For the second problem, the current model performs a binary classification (benign versus malignant) that can be used for early melanoma detection. The model is general enough to be extended to multi-class skin lesion classification. The proposed solution is built around the VGG-Net ConvNet architecture and uses the transfer learning paradigm.\ Finally, this work performs a comparative evaluation of classification \ alone (using the entire image) against a combination of the two approaches (segmentation followed by classification) in order to assess which of them achieves better classification results.

[Source code]

Skin Lesion Detection from Dermoscopic Images using Convolutional Neural Networks from Xavier Giro
}, author = {Romero-Lopez, Adria}, editor = {Xavier Gir{\'o}-i-Nieto and Marques, Oge} }