ISIC2017: Skin Lesion Analysis Towards Melanoma Detection
Codella N and Gutman D and Celebi ME and Helba B and Marchetti MA and Dusza S and Kalloo A and Liopyris K and Mishra N and Kittler H and Halpern A

isic-challenge-2017 (12 files)
ISIC-2017_Test_v2_Data.zip 5.83GB
ISIC-2017_Test_v2_Part1_GroundTruth.zip 19.12MB
ISIC-2017_Test_v2_Part2_GroundTruth.zip 215.35kB
ISIC-2017_Test_v2_Part3_GroundTruth.csv 13.24kB
ISIC-2017_Training_Data.zip 6.23GB
ISIC-2017_Training_Part1_GroundTruth.zip 9.32MB
ISIC-2017_Training_Part2_GroundTruth.zip 312.80kB
ISIC-2017_Training_Part3_GroundTruth.csv 44.04kB
ISIC-2017_Validation_Data.zip 920.90MB
ISIC-2017_Validation_Part1_GroundTruth.zip 572.32kB
ISIC-2017_Validation_Part2_GroundTruth.zip 52.53kB
ISIC-2017_Validation_Part3_GroundTruth.csv 3.34kB
Type: Dataset
Tags:

Bibtex:
@article{,
title= {ISIC2017: Skin Lesion Analysis Towards Melanoma Detection},
keywords= {},
author= {Codella N and Gutman D and Celebi ME and Helba B and Marchetti MA and Dusza S and Kalloo A and Liopyris K and Mishra N and Kittler H and Halpern A},
abstract= {The goal of the challenge is to help participants develop image analysis tools to enable the automated diagnosis of melanoma from dermoscopic images. Image analysis of skin lesions is composed of 3 parts:

- Part 1: Lesion Segmentation
- Part 2: Detection and Localization of Visual Dermoscopic Features/Patterns
- Part 3: Disease Classification

This challenge provides training data (~2000 images) for participants to engage in all 3 components of lesion image analysis. A separate public validation dataset (~150 images) and blind held-out test dataset (~600 images) will be provided for participants to generate and submit automated results.

## Background

### Melanoma
Skin cancer is a major public health problem, with over 5 million newly diagnosed cases in the United States each year. Melanoma is the deadliest form of skin cancer, responsible for over 9,000 deaths each year.

### Dermoscopy
As pigmented lesions occurring on the surface of the skin, melanoma is amenable to early detection by expert visual inspection. It is also amenable to automated detection with image analysis. Given the widespread availability of high-resolution cameras, algorithms that can improve our ability to screen and detect troublesome lesions can be of great value. As a result, many centers have begun their own research efforts on automated analysis. However, a centralized, coordinated, and comparative effort across institutions has yet to be implemented.

Dermoscopy is an imaging technique that eliminates the surface reflection of skin. By removing surface reflection, visualization of deeper levels of skin is enhanced. Prior research has shown that when used by expert dermatologists, dermoscopy provides improved diagnostic accuracy, in comparison to standard photography. As inexpensive consumer dermatoscope attachments for smart phones are beginning to reach the market, the opportunity for automated dermoscopic assessment algorithms to positively influence patient care increases.

https://i.imgur.com/daTTwFV.png

## Citation:

Codella N, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza S, Kalloo A, Liopyris K, Mishra N, Kittler H, Halpern A. "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 [cs.CV] Available: https://arxiv.org/abs/1710.05006
},
terms= {},
license= {},
superseded= {},
url= {}
}


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