AIMC Topic: Nevus

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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 Dec 2, 2020
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...
Humans Aged, 80 and over Retrospective Studies Young Adult Female Adult Infant, Newborn Male Melanoma Middle Aged Skin Neoplasms Dermoscopy Reproducibility of Results Aged Infant Deep Learning Adolescent Child, Preschool Child Diagnosis, Differential Skin Image Interpretation, Computer-Assisted Datasets as Topic Nevus
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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 ...
Supervised Machine Learning Image Interpretation, Computer-Assisted Algorithms Nevus Melanoma Melanocytes Humans Sensitivity and Specificity Deep Learning Skin
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Towards Interpretable Skin Lesion Classification with Deep Learning Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Skin disease is a prevalent condition all over the world. Computer vision-based technology for automatic skin lesion classification holds great promise as an effective screening tool for early diagnosis. In this paper, we propose an accurate and inte...
Models, Biological Humans Skin Diseases Neural Networks, Computer Nevus Skin Neoplasms Keratosis Deep Learning
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Histopathology-guided mass spectrometry differentiates benign nevi from malignant melanoma.

Journal of cutaneous pathology Nov 27, 2019
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 ...
Female Melanoma Skin Neoplasms Aged Aged, 80 and over Humans Machine Learning Adolescent Child, Preschool Child Proteomics Mass Spectrometry Young Adult Adult Diagnosis, Differential Male Middle Aged Melanoma, Cutaneous Malignant Nevus
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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) Aug 14, 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...
Hospitals, University Nevus Surveys and Questionnaires Diagnosis, Differential Biopsy Dermatologists Humans Neural Networks, Computer Algorithms Female Melanoma Skin Neoplasms Dermoscopy Sensitivity and Specificity Male
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Deep neural networks are superior to dermatologists in melanoma image classification.

European journal of cancer (Oxford, England : 1990) Aug 8, 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...
Image Interpretation, Computer-Assisted Nevus Melanoma Skin Neoplasms Dermoscopy Reproducibility of Results Surveys and Questionnaires ROC Curve Biopsy Skin Dermatologists Humans Neural Networks, Computer Algorithms
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Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.

European journal of cancer (Oxford, England : 1990) Jul 18, 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...
Pathologists Microscopy Image Interpretation, Computer-Assisted Observer Variation Nevus Deep Learning Diagnosis, Differential Predictive Value of Tests Biopsy Humans Melanoma Skin Neoplasms Reproducibility of Results Diagnosis, Computer-Assisted
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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 Jul 12, 2019
BACKGROUND: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain.
Humans Melanoma Skin Neoplasms Dermoscopy Cross-Sectional Studies Deep Learning ROC Curve Keratosis, Seborrheic United States Image Interpretation, Computer-Assisted Diagnosis, Differential Spain International Cooperation Internship and Residency Nevus Skin Dermatologists Israel Colombia
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Pathologist-level classification of histopathological melanoma images with deep neural networks.

European journal of cancer (Oxford, England : 1990) May 23, 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% ...
Diagnosis, Computer-Assisted Deep Learning Predictive Value of Tests Biopsy Pathologists Humans Melanoma Skin Neoplasms Reproducibility of Results Microscopy Image Interpretation, Computer-Assisted Observer Variation Nevus
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Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task.

European journal of cancer (Oxford, England : 1990) Apr 10, 2019
BACKGROUND: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For ...
Melanoma Skin Neoplasms Dermoscopy Humans Sensitivity and Specificity Deep Learning Dermatologists Hospitals, University Germany Nevus
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