Fully Convolutional Network based on Contrast Information Integration for Dermoscopic Image Segmentation
Pages 176 - 181
Abstract
Melanoma is one of the most common human lethal cancers. Because the lesions have different shapes, sizes, colors, and low contrast, extracting powerful features for fine-grained skin lesion segmentation is still a challenging task today. In this paper, we propose a novel fully convolutional network based on contrast information integration for skin lesion segmentation, which effectively utilizes contrast information from each convolutional block in our network framework. Compared with existing skin lesion segmentation approaches, a new integration module is designed by combining the contrast information for extracting richer feature representation. Finally, we evaluate our method on the public ISIC 2017 challenge dataset and obtain the outstanding performance with the Jaccard Index (JA) of 79.9%, which is higher than other state-of-the-art methods for skin lesion segmentation.
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Index Terms
- Fully Convolutional Network based on Contrast Information Integration for Dermoscopic Image Segmentation
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Published In
April 2020
252 pages
ISBN:9781450377072
DOI:10.1145/3395260
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In-Cooperation
- Southwest Jiaotong University
- Xihua University: Xihua University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 May 2020
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- Research-article
- Research
- Refereed limited
Funding Sources
- Special Fund Project for Innovation of High-level Overseas Talents
- Shenzhen Discipline Construction Project for Urban Computing and Data Intelligence
- Major Special Project of Guangdong Province
- Shenzhen Basic Research Projects
- Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology
- National Natural Science Foundation of China
- Shenzhen Science and Technology Innovation Project
Conference
ICMAI 2020
ICMAI 2020: 2020 5th International Conference on Mathematics and Artificial Intelligence
April 10 - 13, 2020
Chengdu, China
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