Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
Xinzi HeYiyan Shi

Abstract

Dermoscopy imaging has been a routine examination approach for skin lesion diagnosis. Accurate segmentation is the first step for automatic dermoscopy image assessment. The main challenges for skin lesion segmentation are numerous variations in viewpoint and scale of skin lesion region. To handle these challenges, we propose a novel skin lesion segmentation network via a very deep dense deconvolution network based on dermoscopic images. Specifically, the deep dense layer and generic multi-path Deep RefineNet are combined to improve the segmentation performance. The deep representation of all available layers is aggregated to form the global feature maps using skip connection. Also, the dense deconvolution layer is leveraged to capture diverse appearance features via the contextual information. Finally, we apply the dense deconvolution layer to smooth segmentation maps and obtain final high-resolution output. Our proposed method shows the superiority over the state-of-the-art approaches based on the public available 2016 and 2017 skin lesion challenge dataset and achieves the accuracy of 96.0% and 93.9%, which obtained a 6.0% and 1.2% increase over the traditional method, respectively. By utilizing Dense Deconvolution Net, the a...Continue Reading

References

Jul 22, 2014·Pattern Recognition Letters·Esra Ataer-CansizogluDeniz Erdogmus
May 29, 2015·Nature·Yann LeCunGeoffrey Hinton
Jan 9, 2016·CA: a Cancer Journal for Clinicians·Rebecca L SiegelAhmedin Jemal
Sep 1, 2016·IEEE Journal of Biomedical and Health Informatics·Jun ShiShihui Ying
Dec 28, 2016·IEEE Transactions on Medical Imaging·Lequan YuPheng-Ann Heng
Jan 6, 2017·CA: a Cancer Journal for Clinicians·Rebecca L SiegelAhmedin Jemal
Feb 15, 2017·JAMA Dermatology·Caroline Gaudy-MarquesteJean-Jacques Grob
May 26, 2017·IEEE Transactions on Neural Networks and Learning Systems·Shihui YingHong Qiao

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Software Mentioned

MatConvNet
MATLAB

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