Deep Learning For Skin Cancer Diagnosis With Hierarchical Architectures

Skin lesions are organized in a hierarchical way, which is taken into account by dermatologists when diagnosing them. However, automatic systems do not make use of this information, performing the diagnosis in a one-vs-all approach, where all types of lesions are considered. In this paper we propose to mimic the medical strategy and train a deep-learning architecture to perform a hierarchical diagnosis. Our results highlight the benefits of addressing the classification of dermoscopy images in a structured way. Additionally, we provide an extensive evaluation of criteria that must be taken into account in the development of diagnostic systems based on deep learning.

[1]  Pedro M. Ferreira,et al.  PH2 - A dermoscopic image database for research and benchmarking , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[2]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[3]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[4]  Hiroshi Koga,et al.  Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble , 2017, ArXiv.

[5]  Masaru Tanaka,et al.  Four-Class Classification of Skin Lesions With Task Decomposition Strategy , 2015, IEEE Transactions on Biomedical Engineering.

[6]  P. C. Siddalingaswamy,et al.  Techniques and algorithms for computer aided diagnosis of pigmented skin lesions - A review , 2018, Biomed. Signal Process. Control..

[7]  Eduardo Valle,et al.  Data, Depth, and Design: Learning Reliable Models for Melanoma Screening , 2017, ArXiv.

[8]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[9]  Cristina Nader Vasconcelos,et al.  Experiments using deep learning for dermoscopy image analysis , 2017, Pattern Recognit. Lett..

[10]  Eduardo Valle,et al.  Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes , 2016, ArXiv.

[11]  Angela Ferrari,et al.  Interactive atlas of dermoscopy , 2000 .

[12]  Eduardo Valle,et al.  Data Augmentation for Skin Lesion Analysis , 2018, OR 2.0/CARE/CLIP/ISIC@MICCAI.

[13]  Balázs Harangi,et al.  Skin lesion detection based on an ensemble of deep convolutional neural network , 2017, J. Biomed. Informatics.

[14]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Jorge S. Marques,et al.  Improving Dermoscopy Image Classification Using Color Constancy , 2015, IEEE Journal of Biomedical and Health Informatics.

[16]  Jorge S. Marques,et al.  A Survey of Feature Extraction in Dermoscopy Image Analysis of Skin Cancer , 2019, IEEE Journal of Biomedical and Health Informatics.

[17]  A. Jemal,et al.  Cancer statistics, 2018 , 2018, CA: a cancer journal for clinicians.

[18]  Noel C. F. Codella,et al.  Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) , 2016, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[19]  Rahil Garnavi,et al.  Tree-loss function for training neural networks on weakly-labelled datasets , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[20]  John R. Smith,et al.  Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images , 2015, MLMI.