AIMC Topic: Dermoscopy

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Automated melanoma detection. An algorithm inspired from human intelligence characterizing disordered pattern of melanocytic lesions improving a convolutional neural network.

Journal of the American Academy of Dermatology Apr 12, 2024
Melanoma Humans Neural Networks, Computer Skin Neoplasms Algorithms Dermoscopy
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Dermatoscopic features and potential pitfalls of artificial intelligence-based analysis of benign acral pigmented lesions in Black patients: A multicenter observational study.

Journal of the American Academy of Dermatology Mar 19, 2024
Male Middle Aged Black or African American Dermoscopy Adult Humans Artificial Intelligence Female Skin Neoplasms
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Combining artificial intelligence and human expertise for more accurate dermoscopic melanoma diagnosis: A 2-session retrospective reader study.

Journal of the American Academy of Dermatology Feb 29, 2024
Retrospective Studies Melanoma Skin Neoplasms Dermoscopy Clinical Competence Humans Artificial Intelligence
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LAMA: Lesion-Aware Mixup Augmentation for Skin Lesion Segmentation.

Journal of imaging informatics in medicine Feb 26, 2024
Deep learning can exceed dermatologists' diagnostic accuracy in experimental image environments. However, inaccurate segmentation of images with multiple skin lesions can be seen with current methods. Thus, information present in multiple-lesion imag...
Databases, Factual Skin Neoplasms Dermoscopy Neural Networks, Computer Algorithms Skin Diseases Image Interpretation, Computer-Assisted Deep Learning Image Processing, Computer-Assisted Skin Humans
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Basal Cell Carcinoma Diagnosis with Fusion of Deep Learning and Telangiectasia Features.

Journal of imaging informatics in medicine Feb 8, 2024
In recent years, deep learning (DL) has been used extensively and successfully to diagnose different cancers in dermoscopic images. However, most approaches lack clinical inputs supported by dermatologists that could aid in higher accuracy and explai...
Humans Image Interpretation, Computer-Assisted Telangiectasis Dermoscopy Skin Neoplasms Sensitivity and Specificity Carcinoma, Basal Cell Deep Learning
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Fusion between an Algorithm Based on the Characterization of Melanocytic Lesions' Asymmetry with an Ensemble of Convolutional Neural Networks for Melanoma Detection.

The Journal of investigative dermatology Jan 29, 2024
Melanoma is still a major health problem worldwide. Early diagnosis is the first step toward reducing its mortality, but it remains a challenge even for experienced dermatologists. Although computer-aided systems have been developed to help diagnosis...
Humans Skin Neoplasms Neural Networks, Computer Dermoscopy Algorithms Sensitivity and Specificity Diagnosis, Differential Melanoma Diagnosis, Computer-Assisted Nevus Early Detection of Cancer Nevus, Pigmented
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Understanding skin color bias in deep learning-based skin lesion segmentation.

Computer methods and programs in biomedicine Jan 24, 2024
BACKGROUND: The field of dermatological image analysis using deep neural networks includes the semantic segmentation of skin lesions, pivotal for lesion analysis, pathology inference, and diagnoses. While biases in neural network-based dermatoscopic ...
Humans Dermoscopy Deep Learning Image Processing, Computer-Assisted Skin Skin Diseases Skin Pigmentation
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Using multiple real-world dermoscopic photographs of one lesion improves melanoma classification via deep learning.

Journal of the American Academy of Dermatology Jan 8, 2024
Dermoscopy Algorithms Deep Learning Melanoma Skin Neoplasms Humans
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Artificial intelligence in the detection of skin cancer: State of the art.

Clinics in dermatology Jan 3, 2024
The incidence of melanoma is increasing rapidly. This cancer has a good prognosis if detected early. For this reason, various systems of skin lesion image analysis, which support imaging diagnostics of this neoplasm, are developing very dynamically. ...
Smartphone Humans Skin Neoplasms Artificial Intelligence Dermoscopy Algorithms Melanoma
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Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence.

Journal of the European Academy of Dermatology and Venereology : JEADV Dec 29, 2023
BACKGROUND: Deep-learning convolutional neural networks (CNNs) have outperformed even experienced dermatologists in dermoscopic melanoma detection under controlled conditions. It remains unexplored how real-world dermoscopic image transformations aff...
Deep Learning Dysplastic Nevus Syndrome Middle Aged Risk Assessment Skin Neoplasms Prospective Studies Humans Dermoscopy Aged Female Neural Networks, Computer Adult Melanoma Reproducibility of Results Male
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