mlsanityData to 14 Oct 2026

Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)

arXiv 1605.01397 ↗First posted 4 May 2016cs.CVcited by 198

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Abstract (arXiv)

In this article, we describe the design and implementation of a publicly accessible dermatology image analysis benchmark challenge. The goal of the challenge is to sup- port research and development of algorithms for automated diagnosis of melanoma, a lethal form of skin cancer, from dermoscopic images. The challenge was divided into sub-challenges for each task involved in image analysis, including lesion segmentation, dermoscopic feature detection within a lesion, and classification of melanoma. Training data included 900 images. A separate test dataset of 379 images was provided to measure resultant performance of systems developed with the training data. Ground truth for both training and test sets was generated by a panel of dermoscopic experts. In total, there were 79 submissions from a group of 38 participants, making this the largest standardized and comparative study for melanoma diagnosis in dermoscopic images to date. While the official challenge duration and ranking of participants has concluded, the datasets remain available for further research and development.

Key facts. Cited by 198 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 May 2016.

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Citing papers per year: 2021: 7; 2022: 21; 2023: 41; 2024: 54; 2025: 38; 2026: 37.

198 papers.

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  2. 2022Transformers in Medical Imaging: A Surveycited by 245
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  9. 2022Towards a Guideline for Evaluation Metrics in Medical Image Segmentationcited by 60
  10. 2023CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detectioncited by 58
  11. 2024LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Modelscited by 51
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38 more
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  9. 2022A Survey on Deep Learning for Skin Lesion Segmentationcited by 22
  10. 2024Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detectioncited by 22
  11. 2024Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlookscited by 18
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  13. 2024MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generationcited by 17
  14. 2023Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directionscited by 16
  15. 2024Shortcut Learning in Medical Image Segmentationcited by 16
  16. 2025MIMO: A medical vision language model with visual referring multimodal input and pixel grounding multimodal outputcited by 15
  17. 2021Semi-supervised learning for medical image classification using imbalanced training datacited by 14
  18. 2022MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentationcited by 13
  19. 2024Explainable Artificial Intelligence for Medical Applications: A Reviewcited by 12
  20. 2021Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanningcited by 11
  21. 2023DVPT: Dynamic Visual Prompt Tuning of Large Pre-trained Models for Medical Image Analysiscited by 11
  22. 2022Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scorescited by 10
  23. 2024DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 38 Subclassescited by 10
  24. 2022Segmentation Ability Map: Interpret deep features for medical image segmentationcited by 9
  25. 2023Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentationcited by 9
  26. 2023Attention Mechanisms in Medical Image Segmentation: A Surveycited by 9
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  28. 2022SeATrans: Learning Segmentation-Assisted diagnosis model via Transformercited by 8
  29. 2022Equivariance-aware Architectural Optimization of Neural Networkscited by 8
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  34. 2024TBConvL-Net: A Hybrid Deep Learning Architecture for Robust Medical Image Segmentationcited by 8
  35. 2025In the Picture: Medical Imaging Datasets, Artifacts, and their Living Reviewcited by 8
  36. 2025SAMed-2: Selective Memory Enhanced Medical Segment Anything Modelcited by 8
  37. 2022A Typology for Exploring the Mitigation of Shortcut Behaviorcited by 7
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