AIMC Topic: Skin Neoplasms

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Comparing the capabilities of transfer learning models to detect skin lesion in humans.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine Jul 9, 2020
Effective diagnosis of skin tumours mainly relies on the analysis of the characteristics of the lesion. Automatic detection of malignant skin lesion has become a mandatory task to reduce the risk of human deaths and increase their survival. This arti...
Humans Neural Networks, Computer Machine Learning Skin Neoplasms Skin
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Melanoma detection using adversarial training and deep transfer learning.

Physics in medicine and biology Jul 6, 2020
Skin lesion datasets consist predominantly of normal samples with only a small percentage of abnormal ones, giving rise to the class imbalance problem. Also, skin lesion images are largely similar in overall appearance owing to the low inter-class va...
Melanoma Skin Neoplasms Deep Learning Image Processing, Computer-Assisted Humans
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A deep learning algorithm to detect the presence of basal cell carcinoma on Mohs micrographic surgery frozen sections.

Journal of the American Academy of Dermatology Jun 23, 2020
Frozen Sections Mohs Surgery Image Interpretation, Computer-Assisted False Negative Reactions Proof of Concept Study Skin Neoplasms Carcinoma, Basal Cell Deep Learning Pilot Projects ROC Curve Skin Feasibility Studies Humans Retrospective Studies
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Human-computer collaboration for skin cancer recognition.

Nature medicine Jun 22, 2020
The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI-based support into new paradigms of care. Here we build on recent...
Humans Neural Networks, Computer User-Computer Interface Telemedicine Artificial Intelligence Physicians Clinical Decision-Making Skin Neoplasms
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Past and present of computer-assisted dermoscopic diagnosis: performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesions.

European journal of cancer (Oxford, England : 1990) Jun 10, 2020
BACKGROUND: Convolutional neural networks (CNNs) have shown a dermatologist-level performance in the classification of skin lesions. We aimed to deliver a head-to-head comparison of a conventional image analyser (CIA), which depends on segmentation a...
Humans Databases, Factual Predictive Value of Tests Image Interpretation, Computer-Assisted Diagnosis, Computer-Assisted Aged Deep Learning Aged, 80 and over Adolescent Young Adult Melanoma Adult Skin Neoplasms Cross-Sectional Studies Dermoscopy Prospective Studies Male Reproducibility of Results Prognosis Middle Aged Female
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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...
Support Vector Machine Dermoscopy Reproducibility of Results Sensitivity and Specificity Humans Diagnosis, Computer-Assisted Melanoma Skin Neoplasms
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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...
Dermatitis, Seborrheic Folliculitis Nurse Practitioners Keratosis, Seborrheic Warts Carcinoma, Basal Cell Carcinoma, Squamous Cell Adult Male Middle Aged Deep Learning Indians, North American Humans Physicians, Primary Care Female Alaska Natives Melanoma Eczema Native Hawaiian or Pacific Islander Skin Diseases Skin Neoplasms Acne Vulgaris Black or African American Hispanic or Latino Psoriasis Asian Telemedicine Diagnosis, Differential Photography White People Dermatologists
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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...
Skin Neoplasms Humans Deep Learning Artificial Intelligence Dermatologists Algorithms
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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...
Photography Microscopy, Confocal Humans Biomedical Technology Machine Learning Dielectric Spectroscopy Melanoma Skin Neoplasms Dermoscopy Imaging, Three-Dimensional
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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...
Humans Margins of Excision Sensitivity and Specificity Skin Neoplasms Deep Learning Carcinoma, Basal Cell Plastic Surgery Procedures Feasibility Studies
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