AIMC Topic: Skin Neoplasms

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An Efficient Skin Cancer Diagnostic System Using Bendlet Transform and Support Vector Machine.

Anais da Academia Brasileira de Ciencias May 29, 2020
Skin is the outermost and largest organ of the human body that protects us from the external agents. Among the various types of diseases affecting the skin, melanoma (skin cancer) is the most dangerous and deadliest disease. Though it is one of the d...
Reproducibility of Results Humans Sensitivity and Specificity Melanoma Diagnosis, Computer-Assisted Skin Neoplasms Support Vector Machine Dermoscopy
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A deep learning system for differential diagnosis of skin diseases.

Nature medicine May 18, 2020
Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of sk...
Physicians, Primary Care Alaska Natives Native Hawaiian or Pacific Islander Black or African American Skin Diseases Hispanic or Latino Telemedicine White People Adult Nurse Practitioners Melanoma Male Skin Neoplasms Warts Middle Aged Indians, North American Keratosis, Seborrheic Carcinoma, Basal Cell Humans Dermatitis, Seborrheic Deep Learning Eczema Psoriasis Folliculitis Carcinoma, Squamous Cell Female Acne Vulgaris Diagnosis, Differential Dermatologists Asian Photography
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Deep learning for dermatologists: Part I. Fundamental concepts.

Journal of the American Academy of Dermatology May 17, 2020
Artificial intelligence is generating substantial interest in the field of medicine. One form of artificial intelligence, deep learning, has led to rapid advances in automated image analysis. In 2017, an algorithm demonstrated the ability to diagnose...
Humans Artificial Intelligence Algorithms Skin Neoplasms Deep Learning Dermatologists
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Technological advances for the detection of melanoma: Advances in diagnostic techniques.

Journal of the American Academy of Dermatology Apr 26, 2020
Managing the balance between accurately identifying early stage melanomas while avoiding obtaining biopsy specimens of benign lesions (ie, overbiopsy) is the major challenge of melanoma detection. Decision making can be especially difficult in patien...
Biomedical Technology Dielectric Spectroscopy Photography Skin Neoplasms Dermoscopy Humans Imaging, Three-Dimensional Machine Learning Microscopy, Confocal Melanoma
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Perioperative margin detection in basal cell carcinoma using a deep learning framework: a feasibility study.

International journal of computer assisted radiology and surgery Apr 22, 2020
PURPOSE: Basal cell carcinoma (BCC) is the most commonly diagnosed cancer and the number of diagnosis is growing worldwide due to increased exposure to solar radiation and the aging population. Reduction of positive margin rates when removing BCC lea...
Plastic Surgery Procedures Feasibility Studies Margins of Excision Skin Neoplasms Carcinoma, Basal Cell Sensitivity and Specificity Deep Learning Humans
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New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation.

BioMed research international Apr 13, 2020
In this paper, an algorithm is introduced to solve the global optimization problem for melanoma skin cancer segmentation. The algorithm is based on the smoothing of an auxiliary function that is constructed using a known local minimizer and smoothed ...
Humans Neural Networks, Computer Algorithms Melanoma Skin Neoplasms Diagnosis, Computer-Assisted Image Processing, Computer-Assisted Melanoma, Cutaneous Malignant
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A machine learning algorithm for simulating immunohistochemistry: development of SOX10 virtual IHC and evaluation on primarily melanocytic neoplasms.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Apr 1, 2020
Immunohistochemistry (IHC) is a diagnostic technique used throughout pathology. A machine learning algorithm that could predict individual cell immunophenotype based on hematoxylin and eosin (H&E) staining would save money, time, and reduce tissue co...
Humans Neural Networks, Computer Machine Learning Algorithms Databases, Factual Melanocytes Melanoma SOXE Transcription Factors Skin Neoplasms Staining and Labeling Immunohistochemistry
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Augmented Intelligence Dermatology: Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders.

The Journal of investigative dermatology Mar 31, 2020
Although deep learning algorithms have demonstrated expert-level performance, previous efforts were mostly binary classifications of limited disorders. We trained an algorithm with 220,680 images of 174 disorders and validated it using Edinburgh (1,3...
Drug Therapy, Computer-Assisted Feasibility Studies Dermatologists Skin Diseases Dermatology Image Interpretation, Computer-Assisted Datasets as Topic Photography Internship and Residency ROC Curve Antiviral Agents Female Antifungal Agents Humans Skin Neoplasms Glucocorticoids Skin Young Adult Anti-Bacterial Agents Deep Learning Adult Dermoscopy Clinical Competence Male Aged Adolescent Middle Aged
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[The future of AI in dermatology and of dermatology with AI].

Annales de dermatologie et de venereologie Mar 30, 2020
Forecasting Research Skin Neoplasms Deep Learning Humans Artificial Intelligence Machine Learning Robotics Skin Diseases Dermatology Physician's Role
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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...
Dermoscopy Image Processing, Computer-Assisted Skin Skin Diseases Humans Neural Networks, Computer Algorithms Skin Neoplasms Artifacts
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