Effective Melanoma Recognition Using Deep Convolutional Neural Network with Covariance Discriminant Loss

Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discriminant loss in dermoscopy images. Deep convolutional neural network is trained under the joint supervision of cross entropy loss and covariance discriminant loss, rectifying the model outputs and the extracted features simultaneously. Specifically, we design an embedding loss, namely covariance discriminant loss, which takes the first and second distance into account simultaneously for providing more constraints. By constraining the distance between hard samples and minority class center, the deep features of melanoma and non-melanoma can be separated effectively. To mine the hard samples, we also design the corresponding algorithm. Further, we analyze the relationship between the proposed loss and other losses. On the International Symposium on Biomedical Imaging (ISBI) 2018 Skin Lesion Analysis dataset, the two schemes in the proposed method can yield a sensitivity of 0.942 and 0.917, respectively. The comprehensive results have demonstrated the efficacy of the designed embedding loss and the proposed methodology.

[1]  Chen Huang,et al.  Learning Deep Representation for Imbalanced Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Bai Ying Lei,et al.  Hybrid dermoscopy image classification framework based on deep convolutional neural network and Fisher vector , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[3]  Pietro Rubegni,et al.  Automated diagnosis of pigmented skin lesions , 2002, International journal of cancer.

[4]  Ioannis A. Kakadiaris,et al.  Deep Imbalanced Attribute Classification using Visual Attention Aggregation , 2018, ECCV.

[5]  Xiao Zhang,et al.  Range Loss for Deep Face Recognition with Long-Tailed Training Data , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[6]  Amirreza Mahbod,et al.  Skin Lesion Classification Using Hybrid Deep Neural Networks , 2017, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  Shu-Ching Chen,et al.  Dynamic Sampling in Convolutional Neural Networks for Imbalanced Data Classification , 2018, 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).

[8]  Ghassan Hamarneh,et al.  Melanoma Recognition via Visual Attention , 2019, IPMI.

[9]  G. Zouridakis,et al.  Malignant melanoma detection by Bag-of-Features classification , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  A. Green,et al.  Computer image analysis in the diagnosis of melanoma. , 1994, Journal of the American Academy of Dermatology.

[11]  Peijun Du,et al.  Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging , 2016, Neurocomputing.

[12]  Mohammed Bennamoun,et al.  Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[13]  Jinchang Ren,et al.  ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging , 2012, Knowl. Based Syst..

[14]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Yann LeCun,et al.  Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[16]  Xuelong Li,et al.  Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement , 2018, Pattern Recognit..

[17]  Kay Chen Tan,et al.  Training cost-sensitive Deep Belief Networks on imbalance data problems , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[18]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Harald Kittler,et al.  Descriptor : The HAM 10000 dataset , a large collection of multi-source dermatoscopic images of common pigmented skin lesions , 2018 .

[20]  Ewa Szczurek,et al.  ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning , 2019, Scientific Reports.

[21]  Siegfried Wahl,et al.  Leveraging uncertainty information from deep neural networks for disease detection , 2016, Scientific Reports.

[22]  Bartosz Krawczyk,et al.  Learning from imbalanced data: open challenges and future directions , 2016, Progress in Artificial Intelligence.

[23]  Rahil Garnavi,et al.  Exploiting local and generic features for accurate skin lesions classification using clinical and dermoscopy imaging , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[24]  Matthieu Cord,et al.  WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Yong Xia,et al.  Attention Residual Learning for Skin Lesion Classification , 2019, IEEE Transactions on Medical Imaging.

[26]  Hao Chen,et al.  Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks , 2017, IEEE Transactions on Medical Imaging.

[27]  Syed Omer Gilani,et al.  Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network , 2020, Sensors.

[28]  G. Argenziano,et al.  Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. Comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. , 1998, Archives of dermatology.

[29]  Luc Van Gool,et al.  Weakly Supervised Cascaded Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Noel C. F. Codella,et al.  Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) , 2019, ArXiv.

[31]  Shaogang Gong,et al.  Class Rectification Hard Mining for Imbalanced Deep Learning , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[32]  Longbing Cao,et al.  Training deep neural networks on imbalanced data sets , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[33]  Nils Gessert,et al.  Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting , 2019, IEEE Transactions on Biomedical Engineering.

[34]  Subhransu Maji,et al.  Bilinear CNN Models for Fine-Grained Visual Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[35]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Zhiguo Jiang,et al.  Classification for Dermoscopy Images Using Convolutional Neural Networks Based on Region Average Pooling , 2018, IEEE Access.

[37]  Blockin SVM : Which One Performs Better in Classification of MCCs in Mammogram Imaging , 2022 .

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

[39]  Bin Yang,et al.  Learning to Reweight Examples for Robust Deep Learning , 2018, ICML.

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

[41]  Shin Ando,et al.  Deep Over-sampling Framework for Classifying Imbalanced Data , 2017, ECML/PKDD.

[42]  Min Chen,et al.  Deep Learning for Imbalanced Multimedia Data Classification , 2015, 2015 IEEE International Symposium on Multimedia (ISM).

[43]  Rahil Garnavi,et al.  Classification of dermoscopy patterns using deep convolutional neural networks , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[44]  Martial Hebert,et al.  Learning to Model the Tail , 2017, NIPS.

[45]  A. Jemal,et al.  Colorectal cancer statistics, 2017 , 2017, CA: a cancer journal for clinicians.

[46]  Mohammad H. Jafari,et al.  Melanoma detection by analysis of clinical images using convolutional neural network , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[47]  Randy H. Moss,et al.  A methodological approach to the classification of dermoscopy images , 2007, Comput. Medical Imaging Graph..

[48]  Taghi M. Khoshgoftaar,et al.  Survey on deep learning with class imbalance , 2019, J. Big Data.