Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)
Open accessAbstract
This article describes the design, implementation, and results of the latest installment of the dermoscopic image analysis benchmark challenge. The goal is to support research and development of algorithms for automated diagnosis of melanoma, the most lethal skin cancer. The challenge was divided into 3 tasks: lesion segmentation, feature detection, and disease classification. Participation involved 593 registrations, 81 pre-submissions, 46 finalized submissions (including a 4-page manuscript), and approximately 50 attendees, making this the largest standardized and comparative study in this field to date. While the official challenge duration and ranking of participants has concluded, the dataset snapshots remain available for further research and development.
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Neural networks can leverage additional data by integrating multiple modalities into a general framework.
“Previous works demonstrate the ability of neural networks to leverage additional data by integrating multiple modalities into a general framework.[11,27,28]”
From Multimodal skin lesion classification using deep learning (2018) · cites this paper as [11]
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- Authors
- Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., & Halpern, A.
- Affiliations
- IBM (United States) (US)
- Emory University (US)
- University of Central Arkansas (US)
- Kitware (United States) (US)
- Memorial Sloan Kettering Cancer Center (US)
- Missouri University of Science and Technology (US)
- Medical University of Vienna (AT)
- ORCID
- Venue
- 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
- Publisher
- IEEE
- Published
- 2018-04
- Pages
- 168-172
- OpenAlex
- W2346705140
- Cited by
- 2,620
- References
- 4
- Citations by year
- 2026: 335 · 2025: 402 · 2024: 402 · 2023: 336 · 2022: 263 · 2021: 284 · 2020: 228 · 2019: 188 · 2018: 131 · 2017: 44 · 2016: 5
- FWCI
- 448.07
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- melanoma detection, dermoscopic images, skin lesion segmentation, feature detection, skin cancer classification, benchmark challenge, automated diagnosis, ISIC dataset
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