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Association between different scale bars in dermoscopic images and diagnostic performance of a market-approved deep learning convolutional neural network for melanoma recognition.

European journal of cancer (Oxford, England : 1990) Mar 1, 2021
BACKGROUND: Studies systematically unravelling possible causes for false diagnoses of deep learning convolutional neural networks (CNNs) are scarce, yet needed before broader application.
Predictive Value of Tests Image Interpretation, Computer-Assisted Artifacts Nevus Reproducibility of Results Cross-Sectional Studies Diagnosis, Computer-Assisted Deep Learning Humans Retrospective Studies Melanoma Skin Neoplasms Dermoscopy
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Robustness of convolutional neural networks in recognition of pigmented skin lesions.

European journal of cancer (Oxford, England : 1990) Mar 1, 2021
BACKGROUND: A basic requirement for artificial intelligence (AI)-based image analysis systems, which are to be integrated into clinical practice, is a high robustness. Minor changes in how those images are acquired, for example, during routine skin c...
Diagnosis, Computer-Assisted Humans Diagnosis, Differential Neural Networks, Computer Predictive Value of Tests Melanoma Image Interpretation, Computer-Assisted Nevus Skin Neoplasms Dermoscopy Reproducibility of Results
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A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi.

Journal of dermatological science Feb 1, 2021
BACKGROUND: Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists' experience and fails to achieve adequate...
Skin Neoplasms Dermoscopy Reproducibility of Results Aged Aged, 80 and over Humans Retrospective Studies Female Infant, Newborn Melanoma Diagnosis, Differential Skin Image Interpretation, Computer-Assisted Datasets as Topic Nevus Infant Deep Learning Adolescent Young Adult Child, Preschool Child Adult Male Middle Aged
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Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders.

Sensors (Basel, Switzerland) Mar 11, 2020
In this research, we present a semi-supervised segmentation solution using convolutional autoencoders to solve the problem of segmentation tasks having a small number of ground-truth images. We evaluate the proposed deep network architecture for the ...
Skin Supervised Machine Learning Image Interpretation, Computer-Assisted Nevus Melanocytes Algorithms Melanoma Sensitivity and Specificity Deep Learning Humans
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Histopathology-guided mass spectrometry differentiates benign nevi from malignant melanoma.

Journal of cutaneous pathology Mar 1, 2020
PURPOSE: Distinguishing benign nevi from malignant melanoma using current histopathological criteria may be very challenging and is one the most difficult areas in dermatopathology. The goal of this study was to identify proteomic differences, which ...
Middle Aged Adolescent Child, Preschool Child Proteomics Mass Spectrometry Diagnosis, Differential Melanoma, Cutaneous Malignant Nevus Aged Aged, 80 and over Young Adult Adult Male Machine Learning Female Melanoma Skin Neoplasms Humans
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Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017.

Journal of the American Academy of Dermatology Mar 1, 2020
BACKGROUND: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain.
International Cooperation Humans Internship and Residency ROC Curve Melanoma Nevus United States Skin Neoplasms Keratosis, Seborrheic Diagnosis, Differential Dermoscopy Skin Cross-Sectional Studies Dermatologists Deep Learning Israel Colombia Image Interpretation, Computer-Assisted Spain
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Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks.

European journal of cancer (Oxford, England : 1990) Sep 1, 2019
BACKGROUND: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer s...
Neural Networks, Computer Algorithms Female Melanoma Skin Neoplasms Dermoscopy Sensitivity and Specificity Male Surveys and Questionnaires Humans Biopsy Diagnosis, Differential Dermatologists Hospitals, University Nevus
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Deep neural networks are superior to dermatologists in melanoma image classification.

European journal of cancer (Oxford, England : 1990) Sep 1, 2019
BACKGROUND: Melanoma is the most dangerous type of skin cancer but is curable if detected early. Recent publications demonstrated that artificial intelligence is capable in classifying images of benign nevi and melanoma with dermatologist-level preci...
Humans Skin Dermoscopy Neural Networks, Computer Dermatologists Reproducibility of Results Algorithms Image Interpretation, Computer-Assisted Surveys and Questionnaires Melanoma Nevus ROC Curve Skin Neoplasms Biopsy
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Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.

European journal of cancer (Oxford, England : 1990) Sep 1, 2019
BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25-2...
Microscopy Image Interpretation, Computer-Assisted Observer Variation Nevus Diagnosis, Differential Predictive Value of Tests Biopsy Humans Pathologists Melanoma Skin Neoplasms Reproducibility of Results Diagnosis, Computer-Assisted Deep Learning
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Pathologist-level classification of histopathological melanoma images with deep neural networks.

European journal of cancer (Oxford, England : 1990) Jul 1, 2019
BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports 25-26% ...
Microscopy Diagnosis, Computer-Assisted Humans Image Interpretation, Computer-Assisted Observer Variation Nevus Melanoma Skin Neoplasms Reproducibility of Results Deep Learning Predictive Value of Tests Biopsy Pathologists
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