Dataset Information

The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.

ABSTRACT: Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets 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. Given this diversity we had to apply different acquisition and cleaning methods and developed semi-automatic workflows utilizing specifically trained neural networks. The final dataset consists of 10015 dermatoscopic images which are released as a training set for academic machine learning purposes and are publicly available through the ISIC archive. This benchmark dataset can be used for machine learning and for comparisons with human experts. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions. More than 50% of lesions have been confirmed by pathology, while the ground truth for the rest of the cases was either follow-up, expert consensus, or confirmation by in-vivo confocal microscopy.

SUBMITTER: Tschandl P

PROVIDER: S-EPMC6091241 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.

Tschandl Philipp P Rosendahl Cliff C Kittler Harald H

Scientific data 20180814

Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets 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. Given this diversity we had to apply different acquisition and cleaning methods and developed semi-automat ...[more]

PMID: 30106392

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Project description:Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the performance of a CNN trained with only clinical images of a pigmented skin lesion in a clinical image classification task, in competition with dermatologists, has not been reported to date. In this study, we extracted 5846 clinical images of pigmented skin lesions from 3551 patients. Pigmented skin lesions included malignant tumors (malignant melanoma and basal cell carcinoma) and benign tumors (nevus, seborrhoeic keratosis, senile lentigo, and hematoma/hemangioma). We created the test dataset by randomly selecting 666 patients out of them and picking one image per patient, and created the training dataset by giving bounding-box annotations to the rest of the images (4732 images, 2885 patients). Subsequently, we trained a faster, region-based CNN (FRCNN) with the training dataset and checked the performance of the model on the test dataset. In addition, ten board-certified dermatologists (BCDs) and ten dermatologic trainees (TRNs) took the same tests, and we compared their diagnostic accuracy with FRCNN. For six-class classification, the accuracy of FRCNN was 86.2%, and that of the BCDs and TRNs was 79.5% (p = 0.0081) and 75.1% (p < 0.00001), respectively. For two-class classification (benign or malignant), the accuracy, sensitivity, and specificity were 91.5%, 83.3%, and 94.5% by FRCNN; 86.6%, 86.3%, and 86.6% by BCD; and 85.3%, 83.5%, and 85.9% by TRN, respectively. False positive rates and positive predictive values were 5.5% and 84.7% by FRCNN, 13.4% and 70.5% by BCD, and 14.1% and 68.5% by TRN, respectively. We compared the classification performance of FRCNN with 20 dermatologists. As a result, the classification accuracy of FRCNN was better than that of the dermatologists. In the future, we plan to implement this system in society and have it used by the general public, in order to improve the prognosis of skin cancer.

Project description:1,25-dihydroxyvitamin D3 affects proliferation, differentiation, and apoptosis and protects DNA against oxidative damage with a net tumorostatic and anticarcinogenic effect. It acts through a specific nuclear receptor that is widely distributed through the body. Although a beneficial role of vitamin D in melanoma patients has been suggested, there is lack of information on the changes in the expression pattern of vitamin D receptor during progression of pigmented lesions. Using immunohistochemistry, we analyzed the expression of vitamin D receptor in 140 samples obtained form 82 patients, including 25 benign nevi, 70 primary cutaneous melanomas, 35 metastases, 5 re-excisions, and 5 normal skin biopsies. The strongest expression was observed in normal skin that significantly decreased in melanocytic proliferations with the following order of expression: normal skin > melanocytic nevi > melanomas = metastases. The vitamin D receptor expression in skin surrounding nevi and melanoma was also significantly reduced as compared to normal skin. Tumor-infiltrating and lymph node lymphocytes retained high levels of vitamin D receptor. There was negative correlation between tumor progression and vitamin D receptor expression with a remarkable decrease of the immunoreactivity in nuclei of melanoma cells at vertical versus radial growth phases and with metastatic melanomas showing the lowest cytoplasmic receptor staining. Furthermore, lack of the receptor expression in primary melanomas and metastases was related to shorter overall patients' survival. In addition, the receptor expression decreased in melanized melanoma cells in comparison to amelanotic or poorly pigmented cells. Therefore, we propose that reduction or absence of vitamin D receptor is linked to progression of melanocytic lesions, that its lack affects survival of melanoma patients, and that melanogenesis can attenuate receptor expression. In conclusion, changes in vitamin D receptor expression pattern can serve as important variables for diagnosis, predicting clinical outcome of the disease, and/or as a guidance for novel therapy of melanomas based on use of vitamin D or its derivatives.

Project description:BackgroundWhile skin cancers are less prevalent in people with skin of color, they are more often diagnosed at later stages and have a poorer prognosis. The use of artificial intelligence (AI) models can potentially improve early detection of skin cancers; however, the lack of skin color diversity in training datasets may only widen the pre-existing racial discrepancies in dermatology.ObjectiveThe aim of this study was to systematically review the technique, quality, accuracy, and implications of studies using AI models trained or tested in populations with skin of color for classification of pigmented skin lesions.MethodsPubMed was used to identify any studies describing AI models for classification of pigmented skin lesions. Only studies that used training datasets with at least 10% of images from people with skin of color were eligible. Outcomes on study population, design of AI model, accuracy, and quality of the studies were reviewed.ResultsTwenty-two eligible articles were identified. The majority of studies were trained on datasets obtained from Chinese (7/22), Korean (5/22), and Japanese populations (3/22). Seven studies used diverse datasets containing Fitzpatrick skin type I-III in combination with at least 10% from black Americans, Native Americans, Pacific Islanders, or Fitzpatrick IV-VI. AI models producing binary outcomes (e.g., benign vs. malignant) reported an accuracy ranging from 70% to 99.7%. Accuracy of AI models reporting multiclass outcomes (e.g., specific lesion diagnosis) was lower, ranging from 43% to 93%. Reader studies, where dermatologists' classification is compared with AI model outcomes, reported similar accuracy in one study, higher AI accuracy in three studies, and higher clinician accuracy in two studies. A quality review revealed that dataset description and variety, benchmarking, public evaluation, and healthcare application were frequently not addressed.ConclusionsWhile this review provides promising evidence of accurate AI models in populations with skin of color, the majority of the studies reviewed were obtained from East Asian populations and therefore provide insufficient evidence to comment on the overall accuracy of AI models for darker skin types. Large discrepancies remain in the number of AI models developed in populations with skin of color (particularly Fitzpatrick type IV-VI) compared with those of largely European ancestry. A lack of publicly available datasets from diverse populations is likely a contributing factor, as is the inadequate reporting of patient-level metadata relating to skin color in training datasets.

Dataset Information

The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.

Publications

The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.

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