Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/121484
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Type: Journal article
Title: Attention residual learning for skin lesion classification
Author: Zhang, J.
Xie, Y.
Xia, Y.
Shen, C.
Citation: IEEE Transactions on Medical Imaging, 2019; 38(9):2092-2103
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2019
ISSN: 0278-0062
1558-254X
Statement of
Responsibility: 
Jianpeng Zhang, Yutong Xie, Yong Xia, Chunhua Shen
Abstract: Automated skin lesion classification in dermoscopy images is an essential way to improve the diagnostic performance and reduce melanoma deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs in many image classification tasks, accurate classification of skin lesions remains challenging due to the insufficiency of training data, inter-class similarity, intra-class variation, and the lack of the ability to focus on semantically meaningful lesion parts. To address these issues, we propose an attention residual learning convolutional neural network (ARL-CNN) model for skin lesion classification in dermoscopy images, which is composed of multiple ARL blocks, a global average pooling layer, and a classification layer. Each ARL block jointly uses the residual learning and a novel attention learning mechanisms to improve its ability for discriminative representation. Instead of using extra learnable layers, the proposed attention learning mechanism aims to exploit the intrinsic self-attention ability of DCNNs, i.e., using the feature maps learned by a high layer to generate the attention map for a low layer. We evaluated our ARL-CNN model on the ISIC-skin 2017 dataset. Our results indicate that the proposed ARL-CNN model can adaptively focus on the discriminative parts of skin lesions, and thus achieve the state-of-the-art performance in skin lesion classification.
Keywords: Attention learning; residual learning; skin lesion classification; dermoscopy images
Rights: © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
RMID: 1000000421
DOI: 10.1109/TMI.2019.2893944
Appears in Collections:Computer Science publications

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