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.

## The iToBoS dataset: skin region images extracted from 3D total body photographs for lesion detection The early detection of skin cancer is critical for improving patient outcomes. Traditionally, dermatologists rely on dermoscopy to examine pigmented skin lesions. While this non-invasive technique enhances diagnostic accuracy, its effectiveness is highly dependent on the clinician’s expertise. Additionally, capturing dermoscopic images for every suspicious lesion is a labor-intensive process. Given these challenges, there is an increasing need for computer-aided diagnosis (CAD) systems that utilize conventional cameras. Such systems can support general physicians and other non-specialist practitioners in identifying potential malignant lesion, improving early detection and intervention. Moreover, they facilitate longitudinal tracking of lesions, aiding researchers in studying disease progression and treatment efficacy. This dataset provides high-resolution skin patch images extracted from 3D total body photographs to support the development of advanced machine learning models for lesion detection. It serves as a valuable resource for researchers working on automated skin lesion analysis, particularly in the context of total body photography (TBP). This dataset contains 59,997 lesion identifying regions-of-interest (ROIs) embedded in 16,954 images stemming from 100 patients. ## Dataset Description: The iToBoS dataset consists of 16,954 high-resolution images of skin regions obtained from anonymized 3D avatars of patients. These avatars were generated using the Canfield VECTRA WB360 system, a cutting-edge imaging technology that captures comprehensive, full-body skin images using 92 fixed cameras arranged in 46 stereo pairs with xenon flash lighting. The images were collected from patients at two clinical sites: the Clinical Hospital of Barcelona (Spain) (n=7,729) and the University of Queensland (Australia) (n=9,225). The dataset provides diverse anatomical locations, including the torso, arms, and legs, with each image having an average resolution of 1012x827 pixels and a 45-pixel overlap between adjacent images. The images are extracted from 3D avatars while ensuring compliance with GDPR regulations by automatically removing patient facial features. Each image is accompanied by metadata, including patient age range, body location, and sun damage score, allowing for in-depth analysis and stratification. ## Significance of the Dataset: 1. **Facilitates Automated Skin Lesion Detection:** The dataset supports the development of AI-based lesion detection models that can improve early diagnosis of skin cancer, particularly in regions with limited access to dermatological expertise. 2. **Supports Total Body Photography Research:** Leveraging 3D TBP for lesion detection is an emerging field, and this dataset provides a benchmark for further exploration. 3. **Enhances Machine Learning Applications:** The dataset serves as a benchmark for developing state-of-the-art computer vision and deep learning models for detection of skin lesions. ## PNG Format The image files were originally captured in PNG format, but are published here in compressed JPEG format. Our internal testing indicates that over 97% of the JPEG images achieve a PSNR greater than 35dB when compared to the original PNG versions, while being only ~6% of the original dataset size. Additionally, the original PNG files are available [on Figshare](https://figshare.com/articles/dataset/iToBoS_2024_-_Skin_Lesion_Detection_with_3D-TBP/28452545). ## Funding EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being) - SC1-BHC-06-2020-965221

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

Collection defined by Dr. Jorge A. Rios-Duarte for a research project.

The BCN20000 dataset consists of dermoscopic images of skin lesions taken between 2010 and 2016 at the Hospital Clínic in Barcelona. It aims to tackle the challenge of classifying dermoscopic images of skin cancer under diverse conditions, including lesions in challenging locations (such as nails and mucosa), large lesions exceeding the dermoscopy device's aperture, and hypo-pigmented lesions. A detailed data descriptor is available: Hernández-Pérez, C., Combalia, M., Podlipnik, S. et al. BCN20000: Dermoscopic Lesions in the Wild. Sci Data 11, 641 (2024). https://doi.org/10.1038/s41597-024-03387-w

Training set from the ISIC 2019 Challenge.

Official collection