ISIC-DICM-17K (ISIC Dermoscopic Images and Clinical Metadata 17K) is a curated and balanced dataset derived from the International Skin Imaging Collaboration (ISIC) Archive Gallery. It comprises 17,060 dermoscopic images and clinical metadata (8,530 melanoma and 8,530 non-melanoma classes). For more details, please follow the project’s GitHub repository: [https://github.com/mmu-dermatology-research/isic-dicm-17k](https://github.com/mmu-dermatology-research/isic-dicm-17k) This dataset was used in this study and benchmark to explore the effectiveness of multimodal learning for skin lesion classification: S. Ahammed, X. Cui, W. Lu and M. H. Yap, "Skin Lesion Classification using Dermoscopic Images and Clinical Metadata: Insights from Multimodal Models," 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, TN, USA, 2025, pp. 222-230, DOI: 10.1109/CVPRW67362.2025.00027

These are the images used in the paper: Analysis of the ISIC image datasets: Usage, benchmarks and recommendations Paper Link: https://www.sciencedirect.com/science/article/pii/S1361841521003509 They have also been used by newer versions such as: Skin Lesion Classification Using Dermoscopic Images and Clinical Metadata: Insights from Multimodal Models Paper Link: https://openaccess.thecvf.com/content/CVPR2025W/MULA2025/papers/Ahammed_Skin_Lesion_Classification_Using_Dermoscopic_Images_and_Clinical_Metadata_Insights_CVPRW_2025_paper.pdf https://api.isic-archive.com/collections/469/

This collection contains the 15 images uploaded for "Dermoscopic Features of Infundibulocystic Basal Cell Carcinoma (IBCC): An Observational Study."

150 Seborrheic Keratoses contributed to the ISIC Archive by Dr. Cristián Naverrete et al. for the purpose of annotating dermoscopic features, matched with 150 prior contributed images.

This collection contains all the images associated with the [IMA++ dataset](https://doi.org/10.5281/zenodo.14201692). The **IMA++ dataset** is the largest publicly available multi-annotator skin lesion segmentation (SLS) dataset, collected from the ISIC Archive to facilitate skin lesion image segmentation research. It contains **17,684 segmentation masks** spanning **14,967 dermoscopic images**, where **2,394 dermoscopic images have 2-5 segmentations per image** from the ISIC Archive, annotated by **16 distinct annotators**, with at least one annotation per image. The dataset captures a wide range of segmentation styles influenced by annotator expertise, tools used, and manual review processes, making it a valuable resource for developing and evaluating SLS models.

Dermoscopic lesion images (close-up views of benign and malignant lesions) from the MEL-SELF trial (the Melanoma Self Surveillance trial).

This dataset contains 11,720 segmentation masks created for the ISIC 2018 dataset. The masks were initially generated using a U-Net model trained on the IMA++ dataset, and then manually reviewed, with corrections made where necessary. When lesion boundaries were unknown, a similarity search was performed across the entire IMA++ dataset to find a reference. If the search failed to find a truly similar match, the manual segmentation focused on capturing outlier details in the center of the image, which may bias the data toward the middle of the frame. Easter egg image ISIC_0035068 is intentionally left completely black with no segmentation. Additionally, some minor artefacts are present: certain masks slightly overlap onto the surrounding skin, some edges appear sharp or spiky. If two skin lesions were very close together, they were marked as a single lesion. This is based on the assumption that the main lesion of interest is placed in the center of the image. As a result, smaller lesions near the edges of the image were not always segmented. You can read about duplicate data and other quirks of ISIC 2018 (named as DermaMNIST-E in article) https://www.nature.com/articles/s41597-025-04382-5

Contributed to the ISIC Archive by Dr. Cristián Naverrete et al.