Attention Residual Learning for Skin Lesion Classification

By Jianpeng Zhang, Yutong Xie, Yong Xia, Chunhua Shen. IEEE Transactions on Medical Imaging, 2019

503 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Epidemiology, Oncology

View Paper: https://www.semanticscholar.org/paper/e485fbf1fab3e3910d301989ba98d6509627cfd2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Yutong Xie
  2. Jianpeng Zhang
  3. Yong Xia
  4. Chunhua Shen
  5. Anton van den Hengel
  6. Qi Wu
  7. Damien Teney
  8. Liang-Chieh Chen

In Epidemiology

  1. Chunhua Shen

Citations in the corpus