TY - GEN
T1 - Skin lesion classification in dermoscopy images using synergic deep learning
AU - Zhang, Jianpeng
AU - Xie, Yutong
AU - Wu, Qi
AU - Xia, Yong
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Automated skin lesion classification in the dermoscopy images is an essential way to improve diagnostic performance and reduce melanoma deaths. Although deep learning has shown proven advantages over traditional methods, which rely on handcrafted features, in image classification, it remains challenging to classify skin lesions due to the significant intra-class variation and inter-class similarity. In this paper, we propose a synergic deep learning (SDL) model to address this issue, which not only uses dual deep convolutional neural networks (DCNNs) but also enables them to mutually learn from each other. Specifically, we concatenate the image representation learned by both DCNNs as the input of a synergic network, which has a fully connected structure and predicts whether the pair of input images belong to the same class. We train the SDL model in the end-to-end manner under the supervision of the classification error in each DCNN and the synergic error. We evaluated our SDL model on the ISIC 2016 Skin Lesion Classification dataset and achieved the state-of-the-art performance.
AB - Automated skin lesion classification in the dermoscopy images is an essential way to improve diagnostic performance and reduce melanoma deaths. Although deep learning has shown proven advantages over traditional methods, which rely on handcrafted features, in image classification, it remains challenging to classify skin lesions due to the significant intra-class variation and inter-class similarity. In this paper, we propose a synergic deep learning (SDL) model to address this issue, which not only uses dual deep convolutional neural networks (DCNNs) but also enables them to mutually learn from each other. Specifically, we concatenate the image representation learned by both DCNNs as the input of a synergic network, which has a fully connected structure and predicts whether the pair of input images belong to the same class. We train the SDL model in the end-to-end manner under the supervision of the classification error in each DCNN and the synergic error. We evaluated our SDL model on the ISIC 2016 Skin Lesion Classification dataset and achieved the state-of-the-art performance.
UR - http://www.scopus.com/inward/record.url?scp=85054071222&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00934-2_2
DO - 10.1007/978-3-030-00934-2_2
M3 - 会议稿件
AN - SCOPUS:85054071222
SN - 9783030009335
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 12
EP - 20
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Fichtinger, Gabor
A2 - Davatzikos, Christos
A2 - Alberola-López, Carlos
A2 - Frangi, Alejandro F.
A2 - Schnabel, Julia A.
PB - Springer Verlag
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -