AIMC Topic: Dermoscopy

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Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network.

Sensors (Basel, Switzerland) Mar 13, 2020
Clinical treatment of skin lesion is primarily dependent on timely detection and delimitation of lesion boundaries for accurate cancerous region localization. Prevalence of skin cancer is on the higher side, especially that of melanoma, which is aggr...
Skin Skin Diseases Artifacts Skin Neoplasms Humans Dermoscopy Neural Networks, Computer Image Processing, Computer-Assisted Algorithms
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DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images.

BMC bioinformatics Mar 11, 2020
BACKGROUND: Melanoma results in the vast majority of skin cancer deaths during the last decades, even though this disease accounts for only one percent of all skin cancers' instances. The survival rates of melanoma from early to terminal stages is mo...
Humans Dermoscopy Deep Learning ROC Curve Area Under Curve Databases, Factual Melanoma Skin Neoplasms
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GP-CNN-DTEL: Global-Part CNN Model With Data-Transformed Ensemble Learning for Skin Lesion Classification.

IEEE journal of biomedical and health informatics Feb 28, 2020
Precise skin lesion classification is still challenging due to two problems, i.e., (1) inter-class similarity and intra-class variation of skin lesion images, and (2) the weak generalization ability of single Deep Convolutional Neural Network trained...
Deep Learning Skin Diseases Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Dermoscopy
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An End-to-End Multi-Task Deep Learning Framework for Skin Lesion Analysis.

IEEE journal of biomedical and health informatics Feb 13, 2020
Automatic skin lesion analysis of dermoscopy images remains a challenging topic. In this paper, we propose an end-to-end multi-task deep learning framework for automatic skin lesion analysis. The proposed framework can perform skin lesion detection, ...
Deep Learning Image Interpretation, Computer-Assisted Melanoma Skin Neoplasms Humans Dermoscopy Algorithms Databases, Factual
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Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas.

Journal of the European Academy of Dermatology and Venereology : JEADV Jan 21, 2020
BACKGROUND: Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not be...
Dermatologists Melanocytes Nevus, Pigmented Diagnosis, Computer-Assisted Deep Learning Clinical Competence Aged Young Adult Adult Sensitivity and Specificity Male Middle Aged Melanoma Skin Neoplasms Dermoscopy Humans Female
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Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy.

The Journal of investigative dermatology Dec 12, 2019
In vivo reflectance confocal microscopy (RCM) enables clinicians to examine lesions' morphological and cytological information in epidermal and dermal layers while reducing the need for biopsies. As RCM is being adopted more widely, the workflow is e...
Humans Feasibility Studies Machine Learning Skin Diseases Dermoscopy Microscopy, Confocal Image Processing, Computer-Assisted Quality Control Diagnosis, Differential Skin
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Skin lesion segmentation using high-resolution convolutional neural network.

Computer methods and programs in biomedicine Dec 4, 2019
BACKGROUND AND OBJECTIVE: Skin lesion segmentation is an important but challenging task in computer-aided diagnosis of dermoscopy images. Many segmentation methods based on convolutional neural networks often fail to extract accurate lesion boundarie...
Artifacts Humans Neural Networks, Computer Algorithms Dermoscopy Diagnosis, Computer-Assisted Skin Diseases
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Dense pooling layers in fully convolutional network for skin lesion segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Oct 7, 2019
One of the essential tasks in medical image analysis is segmentation and accurate detection of borders. Lesion segmentation in skin images is an essential step in the computerized detection of skin cancer. However, many of the state-of-the-art segmen...
Image Interpretation, Computer-Assisted Humans Melanoma Neural Networks, Computer Skin Neoplasms Dermoscopy
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Assessment of Accuracy of an Artificial Intelligence Algorithm to Detect Melanoma in Images of Skin Lesions.

JAMA network open Oct 2, 2019
IMPORTANCE: A high proportion of suspicious pigmented skin lesions referred for investigation are benign. Techniques to improve the accuracy of melanoma diagnoses throughout the patient pathway are needed to reduce the pressure on secondary care and ...
Humans Prospective Studies Biopsy ROC Curve Dermoscopy Photography Smartphone Aged Female Male Area Under Curve Melanoma Middle Aged Skin Neoplasms Deep Learning Adult
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Superior skin cancer classification by the combination of human and artificial intelligence.

European journal of cancer (Oxford, England : 1990) Sep 10, 2019
BACKGROUND: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. Howe...
Deep Learning Prognosis Humans Observer Variation Dermatologists Skin Neoplasms Neural Networks, Computer Image Interpretation, Computer-Assisted Algorithms Dermoscopy
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