mlsanityData to 7 Oct 2026

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)

arXiv 1710.05006 ↗First posted 13 Oct 2017cs.CVcited by 549

Not on the map. Why?

Cited by 27 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 549 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Oct 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 64; 2022: 92; 2023: 97; 2024: 111; 2025: 115; 2026: 70.

549 papers.

Sort:

By area:

  1. 2021The Medical Segmentation Decathloncited by 620
  2. 2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 485
  3. 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 351
  4. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  5. 2022Medical Image Segmentation Review: The success of U-Netcited by 190
  6. 2022UNeXt: MLP-based Rapid Medical Image Segmentation Networkcited by 185
  7. 2021Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Datasetcited by 184
  8. 2024OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLMcited by 182
  9. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
  10. 2023Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applicationscited by 121
  11. 2022CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attentioncited by 103
  12. 2022HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentationcited by 94
38 more
  1. 2024MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicinecited by 79
  2. 2023Semantic Segmentation using Vision Transformers: A surveycited by 73
  3. 2025The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMscited by 72
  4. 2024Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Modelcited by 67
  5. 2024EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentationcited by 64
  6. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  7. 2022What Makes Transfer Learning Work For Medical Images: Feature Reuse & Other Factorscited by 61
  8. 2021Is it Time to Replace CNNs with Transformers for Medical Images?cited by 60
  9. 2023A Review of Uncertainty Estimation and its Application in Medical Imagingcited by 59
  10. 2022Stepwise Feature Fusion: Local Guides Globalcited by 56
  11. 2021UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barriercited by 55
  12. 2023Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generationcited by 54
  13. 2023SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million maskscited by 54
  14. 2021MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentationcited by 53
  15. 2021A Survey on Ensemble Learning under the Era of Deep Learningcited by 47
  16. 2023ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Imagecited by 47
  17. 2024UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentationcited by 45
  18. 2024MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imagingcited by 45
  19. 2023Are Natural Domain Foundation Models Useful for Medical Image Classification?cited by 44
  20. 2022TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentationcited by 43
  21. 2022Transformers in Medical Image Analysis: A Reviewcited by 41
  22. 2022VeriFi: Towards Verifiable Federated Unlearningcited by 41
  23. 2022Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imagingcited by 39
  24. 2023Segment Anything Model for Medical Images?cited by 39
  25. 2021Is segmentation uncertainty useful?cited by 37
  26. 2022DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentationcited by 37
  27. 2021FANet: A Feedback Attention Network for Improved Biomedical Image Segmentationcited by 36
  28. 2022Going Beyond XAI: A Systematic Survey for Explanation-Guided Learningcited by 35
  29. 2023Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentationcited by 35
  30. 2021Feature-based Style Randomization for Domain Generalizationcited by 34
  31. 2022TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Modelcited by 34
  32. 2024H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentationcited by 34
  33. 2022Contextual Attention Network: Transformer Meets U-Netcited by 33
  34. 2023FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneitycited by 33
  35. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  36. 2021Representation Matters: Assessing the Importance of Subgroup Allocations in Training Datacited by 31
  37. 2022The Fully Convolutional Transformer for Medical Image Segmentationcited by 31
  38. 2025Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detectioncited by 31

50 most cited of 549 All 549 (JSON)