• Corpus ID: 34764232

Araguaia Medical Vision Lab at ISIC 2017 Skin Lesion Classification Challenge

@article{Sousa2017AraguaiaMV,
  title={Araguaia Medical Vision Lab at ISIC 2017 Skin Lesion Classification Challenge},
  author={Rafael Teixeira Sousa and Larissa Vasconcellos de Moraes},
  journal={ArXiv},
  year={2017},
  volume={abs/1703.00856}
}
This paper describes the participation of Araguaia Medical Vision Lab at the International Skin Imaging Collaboration 2017 Skin Lesion Challenge. We describe the use of deep convolutional neural networks in attempt to classify images of Melanoma and Seborrheic Keratosis lesions. With use of finetuned GoogleNet and AlexNet we attained results of 0.950 and 0.846 AUC on Seborrheic Keratosis and Melanoma respectively. 
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10 Citations
Background Citations
3
Methods Citations
1
Results Citations
1

10 Citations

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Skin lesion classification with ensembles of deep convolutional neural networks

  • B. Harangi
  • Computer Science
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  • 2018

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A Survey of Feature Extraction in Dermoscopy Image Analysis of Skin Cancer

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Unsupervised Domain Adaptation to Classify Medical Images Using Zero-Bias Convolutional Auto-Encoders and Context-Based Feature Augmentation

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Accuracy of Computer-Aided Diagnosis of Melanoma: A Meta-analysis.

Although the accuracy of computer-aided diagnosis for melanoma detection is comparable to that of experts, the real-world applicability of these systems is unknown and potentially limited owing to overfitting and the risk of bias of the studies at hand.

Sparsity-based Convolutional Kernel Network for Unsupervised Medical Image Analysis

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