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Deep learning based feature representation for automated skin histopathological image annotation

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Abstract

Automated annotation of skin biopsy histopathological images provides valuable information and supports for diagnosis, especially for the discrimination between malignant and benign lesions. Currently, computer-aid analysis of skin biopsy images mostly relied on some human-designed features, which requires expensive human efforts and experiences in problem domains. In this study, we propose an annotation framework for automated skin biopsy image analysis which makes use of a deep model for image feature representation. A convolutional neural network (CNN) is designed for local regions of skin biopsy images which learns potential high-level features automatically from input raw pixels. The annotation model is constructed in the multiple-instance multiple-label (MIML) learning framework with the features learned through the network. We achieve significant improvement of the model performance on a real world clinical skin biopsy image dataset and a benchmark dataset. Moreover, our study indicates that deep learning based model could achieve better performance than human designed features.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (No. 61502106, 81373883, 81573827), Natural Science Foundation of GuangDong Province (No. 2016A030310340), the College Student Career and Innovation Training Plan Project of Guangdong Province (xj201511845018, yj201511845038, yj201611845074, yj201611845075, yj201611845366), the Special Fund of Cultivation of Technology Innovation for University Students (pdjh2016b0150), the 2015 Research Project of Guangdong Education Evaluation Association (No. G-11) and Fujian Major Project of Regional Industry (No. 2014H4015).

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Correspondence to Xianghan Zheng.

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Zhang, G., Hsu, CH.R., Lai, H. et al. Deep learning based feature representation for automated skin histopathological image annotation. Multimed Tools Appl 77, 9849–9869 (2018). https://doi.org/10.1007/s11042-017-4788-5

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  • DOI: https://doi.org/10.1007/s11042-017-4788-5

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