@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} } @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} }