A public-access archive of skin lesion images, supporting teaching, research, and the development and evaluation of diagnostic algorithms.

Skin lesion analysis towards melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC).

Dataset used for Task 1 on the ISIC 2016 Challenge [1].

[1] Gutman, David; Codella, Noel C. F.; Celebi, Emre; Helba, Brian; Marchetti, Michael; Mishra, Nabin; Halpern, Allan. "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)". eprint arXiv:1605.01397. 2016.

This challenge is broken into three separate tasks: - Task 1: Lesion Segmentation - Task 2: Lesion Attribute Detection - Task 3: Disease Classification When using the ISIC 2018 datasets in your research, please cite the following works: [1] Noel Codella, Veronica Rotemberg, Philipp Tschandl, M. Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, Harald Kittler, Allan Halpern: “Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)”, 2018; [2] Tschandl, P., Rosendahl, C. & Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 doi:10.1038/sdata.2018.161 (2018).

Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic).

Diagnostic Categories:

Melanoma Melanocytic nevus Basal cell carcinoma Actinic keratosis Benign keratosis (solar lentigo / seborrheic keratosis / lichen planus-like keratosis) Dermatofibroma Vascular lesion Squamous cell carcinoma

Original Data Source

• Original Challenge: https://challenge.isic-archive.com/data/#2019

  [1] Tschandl P., Rosendahl C. & Kittler H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 doi.10.1038/sdata.2018.161 (2018)

  [2] Noel C. F. Codella, David Gutman, M. Emre Celebi, Brian Helba, Michael A. Marchetti, Stephen W. Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, Allan Halpern: "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)", 2017; arXiv:1710.05006.

  [3] Marc Combalia, Noel C. F. Codella, Veronica Rotemberg, Brian Helba, Veronica Vilaplana, Ofer Reiter, Allan C. Halpern, Susana Puig, Josep Malvehy: "BCN20000: Dermoscopic Lesions in the Wild", 2019; arXiv:1908.02288.

Copyright and Attribution

If you use this dataset in your research, please credit the authors

what-are-the-different-types-of-skin-cancer?

https://www.everydayhealth.com/skin-cancer/what-are-the-different-types-of-skin-cancer/

About this Dataset

This dataset contains the training data for the ISIC 2019 challenge, note that it already includes data from previous years (2018 and 2017).

The dataset for ISIC 2019 contains 25,331 images available for the classification of dermoscopic images among nine different diagnostic categories: - Melanoma - Melanocytic nevus - Basal cell carcinoma - Actinic keratosis - Benign keratosis (solar lentigo / seborrheic keratosis / lichen planus-like keratosis) - Dermatofibroma - Vascular lesion - Squamous cell carcinoma - None of the above

How to use this dataset

• Merge External Data by Alex Shonenkov
• More datasets

Copyright and Attribution

If you use this dataset in your research, please credit the authors

Citations

• [1] Tschandl P., Rosendahl C. & Kittler H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 doi.10.1038/sdata.2018.161 (2018)
• [2] Noel C. F. Codella, David Gutman, M. Emre Celebi, Brian Helba, Michael A. Marchetti, Stephen W. Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, Allan Halpern: “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)”, 2017; arXiv:1710.05006.
• [3] Marc Combalia, Noel C. F. Codella, Veronica Rotemberg, Brian Helba, Veronica Vilaplana, Ofer Reiter, Allan C. Halpern, Susana Puig, Josep Malvehy: “BCN20000: Dermoscopic Lesions in the Wild”, 2019; arXiv:1908.02288.

Image Sources

• BCN_20000 Dataset: (c) Department of Dermatology, Hospital Clínic de Barcelona
• HAM10000 Dataset: (c) by ViDIR Group, Department of Dermatology, Medical University of Vienna; https://doi.org/10.1038/sdata.2018.161
• MSK Dataset: (c) Anonymous; https://arxiv.org/abs/1710.05006 ; https://arxiv.org/abs/1902.03368

License

CC BY NC 4.0

Splash banner

Photo by Carolina Heza on Unsplash

Relevant Links

HAM10000 article MSK article For more datasets, click here.

This dataset is made using raw data from ISIC(The International Skin Imaging Collaboration), an academia and industry partnership designed to use digital skin imaging to help reduce skin cancer mortality. For each image in the original raw dataset we used the associated metadata to generate a simulated conversation about the image between a user and a chatbot.

📄 Context Skin diseases are among the most common health concerns worldwide, ranging from benign lesions like keratosis to serious conditions such as melanoma. Early and accurate diagnosis plays a vital role in preventing disease progression and improving patient outcomes. This dataset aims to assist in developing AI-driven dermatology tools by providing structured information on various skin diseases, their definitions, patient-described symptoms, and associated clinical images. 🔍 Sources The dataset is compiled from a combination of: Publicly available dermatological image repositories, such as the ISIC (International Skin Imaging Collaboration) archive, which contains labeled dermoscopic images of skin lesions. Clinical literature and dermatology textbooks, used to write concise disease definitions. Simulated patient statements, reflecting typical ways in which patients describe their skin conditions during clinical consultations. These were generated based on clinical case studies and patient interviews found in dermatology research papers. Synthetic aggregation: File names refer to images associated with each disease class, meant for easy integration with machine learning pipelines. 🌟 Inspiration This dataset was inspired by the growing need for: Explainable AI (XAI) in dermatology: Making machine learning models more understandable to clinicians and patients. Bridging the gap between clinical terminology and patient language: Helping AI models learn how real patients describe their symptoms, enhancing the usability of teledermatology tools. Supporting education and research: Assisting medical students, researchers, and AI developers in understanding skin diseases in both clinical and layman contexts. Enabling multi-modal learning: Combining text descriptions, disease definitions, and images to train more robust models that can reason across data types. 📄 Column Descriptions Disease Class - The name of the skin disease type (e.g., Actinic Keratosis, Melanoma, Benign Keratosis, etc.). There are 9 unique classes. Disease Definition - A clinical description explaining the nature and characteristics of the disease. Major Statement - Simulated patient descriptions or questions that reflect how individuals typically describe their symptoms. File Name - The corresponding image file name related to the disease case

ISIC 2017 is a large-scale dataset of dermoscopic images published by the International Skin Imaging Collaboration (ISIC). The ISIC 2017 Task 1 dataset is used for the lesion segmentation task and contains 2,000 images for training with ground truth segmentation (2,000 binary mask images).

• 1. Dataset Overview This dataset is organized based on the edge detection task, aiming to provide rich image resources and corresponding edge detection annotation information for related research and applications, which can be used for the testing of edge detection algorithms. In order to evaluate the performance of the edge detection method comprehensively, we created the Medical Image Edge Detection Test (MIEDT) dataset. The MIEDT contains 100 medical images, which were randomly selected from three publicly available datasets, Head CT-hemorrhage, Coronary Artery Diseases DataSet, and Skin Cancer MNIST: HAM10000 .
• 1. Data Set Structure Original image: This folder stores the original image data. It contains 15 Head CT images in PNG format with varying image resolutions; 25 coronary heart disease images in JPG format and with an image resolution of [1024 * 1024]; 60 skin images in JPG format and with an image resolution of [600 * 450]. It covers a variety of medical image materials with different imaging and contrast, providing diverse input data for edge detection algorithms. Ground truth：The data in this folder are the edge detection annotation images corresponding to the images in the "Originals" folder. They are in PNG format. In these images, the white pixels represent the edge parts of the image, and the black pixels represent the non-edge areas. These annotation information accurately outlines the object contours and edge features in the original images.
• 1. Usage Instructions For users who conduct image processing using Python, they can utilize the cv2 (OpenCV) library to read image data. The sample code is as follows:

import cv2 original_image = cv2.imread('Original image/IMG-001.png') # Read original image ground_truth_image = cv2.imread('Ground truth/GT-001.png', cv2.IMREAD_GRAYSCALE) # Read the corresponding Ground Truth image When performing model training based on deep learning frameworks (such as TensorFlow, PyTorch), the dataset path can be configured into the corresponding dataset loading class according to the data loading mechanism of the framework to ensure that the model can correctly read and process the image and its annotation data.

• 4. Data Sources and References Data Sources: The original images are collected from public image datasets Head CT-hemorrhage, Coronary Artery Diseases DataSet, and Skin Cancer MNIST: HAM10000 to ensure the quality and diversity of the images. If you are using this dataset in academic research, please cite the following literature.

References: [1] Noel Codella, Veronica Rotemberg, Philipp Tschandl, M. Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, Harald Kittler, Allan Halpern: “Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)”, 2018; https://arxiv.org/abs/1902.03368

[2] Tschandl, P., Rosendahl, C. & Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 doi:10.1038/sdata.2018.161 (2018).

[3] Classification of Brain Hemorrhage Using Deep Learning from CT Scan Images - https://link.springer.com/chapter/10.1007/978-981-19-7528-8_15

This data repository for MedMNIST v1 is out of date! Please check the latest version of MedMNIST v2.

Abstract

We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28x28 images, which requires no background knowledge. Covering the primary data modalities in medical image analysis, it is diverse on data scale (from 100 to 100,000) and tasks (binary/multi-class, ordinal regression and multi-label). MedMNIST could be used for educational purpose, rapid prototyping, multi-modal machine learning or AutoML in medical image analysis. Moreover, MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets; We have compared several baseline methods, including open-source or commercial AutoML tools. The datasets, evaluation code and baseline methods for MedMNIST are publicly available at https://medmnist.github.io/.

Please note that this dataset is NOT intended for clinical use.

We recommend our official code to download, parse and use the MedMNIST dataset:

pip install medmnist

Citation and Licenses

If you find this project useful, please cite our ISBI'21 paper as: Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis," arXiv preprint arXiv:2010.14925, 2020.

or using bibtex: @article{medmnist, title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis}, author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing}, journal={arXiv preprint arXiv:2010.14925}, year={2020} }

Besides, please cite the corresponding paper if you use any subset of MedMNIST. Each subset uses the same license as that of the source dataset.

PathMNIST

Jakob Nikolas Kather, Johannes Krisam, et al., "Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study," PLOS Medicine, vol. 16, no. 1, pp. 1–22, 01 2019.

License: CC BY 4.0

ChestMNIST

Xiaosong Wang, Yifan Peng, et al., "Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases," in CVPR, 2017, pp. 3462–3471.

License: CC0 1.0

DermaMNIST

Philipp Tschandl, Cliff Rosendahl, and Harald Kittler, "The ham10000 dataset, a large collection of multisource dermatoscopic images of common pigmented skin lesions," Scientific data, vol. 5, pp. 180161, 2018.

Noel Codella, Veronica Rotemberg, Philipp Tschandl, M. Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, Harald Kittler, and Allan Halpern: “Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)”, 2018; arXiv:1902.03368.

License: CC BY-NC 4.0

OCTMNIST/PneumoniaMNIST

Daniel S. Kermany, Michael Goldbaum, et al., "Identifying medical diagnoses and treatable diseases by image-based deep learning," Cell, vol. 172, no. 5, pp. 1122 – 1131.e9, 2018.

License: CC BY 4.0

RetinaMNIST

DeepDR Diabetic Retinopathy Image Dataset (DeepDRiD), "The 2nd diabetic retinopathy – grading and image quality estimation challenge," https://isbi.deepdr.org/data.html, 2020.

License: CC BY 4.0

BreastMNIST

Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy, "Dataset of breast ultrasound images," Data in Brief, vol. 28, pp. 104863, 2020.

License: CC BY 4.0

OrganMNIST_{Axial,Coronal,Sagittal}

Patrick Bilic, Patrick Ferdinand Christ, et al., "The liver tumor segmentation benchmark (lits)," arXiv preprint arXiv:1901.04056, 2019.

Xuanang Xu, Fugen Zhou, et al., "Efficient multiple organ localization in ct image using 3d region proposal network," IEEE Transactions on Medical Imaging, vol. 38, no. 8, pp. 1885–1898, 2019.

License: CC BY 4.0

Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset. We collected dermatoscopic images from different populations, acquired and stored by different modalities. The final dataset consists of 10015 dermatoscopic images which can serve as a training set for academic machine learning purposes. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions: Actinic keratoses and intraepithelial carcinoma / Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, vasc). More than 50% of lesions are confirmed through histopathology (histo), the ground truth for the rest of the cases is either follow-up examination (follow_up), expert consensus (consensus), or confirmation by in-vivo confocal microscopy (confocal). The dataset includes lesions with multiple images, which can be tracked by the lesion_id-column within the HAM10000_metadata file. Due to upload size limitations, images are stored in two files: HAM10000_images_part1.zip (5000 JPEG files) HAM10000_images_part2.zip (5015 JPEG files) Additional data for evaluation purposes The HAM10000 dataset served as the training set for the ISIC 2018 challenge (Task 3). The test-set images are available herein as ISIC2018_Task3_Test_Images.zip (1511 images), the official validation-set is available through the challenge website https://challenge2018.isic-archive.com/. The ISIC-Archive also provides a "Live challenge" submission site for continuous evaluation of automated classifiers on the official validation- and test-set. Comparison to physicians Test-set evaluations of the ISIC 2018 challenge were compared to physicians on an international scale, where the majority of challenge participants outperformed expert readers: Tschandl P. et al., Lancet Oncol 2019 Human-computer collaboration The test-set images were also used in a study comparing different methods and scenarios of human-computer collaboration: Tschandl P. et al., Nature Medicine 2020 Following corresponding metadata is available herein: ISIC2018_Task3_Test_NatureMedicine_AI_Interaction_Benefit.csv: Human ratings for Test images with and without interaction with a ResNet34 CNN (Malignancy Probability, Multi-Class probability, CBIR) or Human-Crowd Multi-Class probabilities. This is data was collected for and analyzed in Tschandl P. et al., Nature Medicine 2020, therefore please refer to this publication when using the data. HAM10000_segmentations_lesion_tschandl.zip: To evaluate regions of CNN activations in Tschandl P. et al., Nature Medicine 2020 (please refer to this publication when using the data), a single dermatologist (Tschandl P) created binary segmentation masks for all 10015 images from the HAM10000 dataset. Masks were initialized with the segmentation network as described by Tschandl et al., Computers in Biology and Medicine 2019, and following verified, corrected or replaced via the free-hand selection tool in FIJI.