AIMC Topic: Skin Neoplasms

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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 ...
Humans Melanoma Skin Neoplasms Dermatologists Dermoscopy Hospitals, University Sensitivity and Specificity Germany Deep Learning Nevus
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A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task.

European journal of cancer (Oxford, England : 1990) Mar 8, 2019
BACKGROUND: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively ...
Humans Neural Networks, Computer Melanoma Dermatologists Dermatology Image Interpretation, Computer-Assisted Skin Neoplasms Dermoscopy Sensitivity and Specificity
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Does integral affect influence intentions to use artificial intelligence for skin cancer screening? A test of the affect heuristic.

Psychology & health Mar 1, 2019
This study investigated how influences people's processing of messages about risks and benefits of using autonomous artificial intelligence (AI) technology to screen for skin cancer. We examined (emotion derived during decision making) separately ...
Affect Humans Early Detection of Cancer Artificial Intelligence Male Female Heuristics Risk Assessment Skin Neoplasms Intention Adult
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Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark.

European journal of cancer (Oxford, England : 1990) Feb 22, 2019
BACKGROUND: Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-certified dermatologists. However, the non-availability of a public human benchmark restricts the comp...
Melanoma Skin Neoplasms Sensitivity and Specificity Deep Learning Humans Germany Benchmarking Dermatologists Dermatology
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Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: Laboratory and prospective observational studies.

EBioMedicine Jan 20, 2019
BACKGROUND: Early diagnosis of skin cancer lesions by dermoscopy, the gold standard in dermatological imaging, calls for a diagnostic upscale. The aim of the study was to improve the accuracy of dermoscopic skin cancer diagnosis through use of novel ...
Sound Telemedicine Adult Male Middle Aged ROC Curve Skin Deep Learning Adolescent Aged Aged, 80 and over Young Adult Female Skin Neoplasms Dermoscopy Algorithms Retrospective Studies Humans Artificial Intelligence
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Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering.

International journal of medical informatics Jan 18, 2019
OBJECTIVE: Melanoma is a dangerous form of the skin cancer responsible for thousands of deaths every year. Early detection of melanoma is possible through visual inspection of pigmented lesions over the skin, treated with simple excision of the cance...
Sensitivity and Specificity Fuzzy Logic Cluster Analysis Neural Networks, Computer Melanoma Skin Neoplasms Humans Dermoscopy
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Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review.

Journal of medical Internet research Oct 17, 2018
BACKGROUND: State-of-the-art classifiers based on convolutional neural networks (CNNs) were shown to classify images of skin cancer on par with dermatologists and could enable lifesaving and fast diagnoses, even outside the hospital via installation ...
Humans Neural Networks, Computer Skin Neoplasms Reproducibility of Results
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Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis.

The British journal of dermatology Sep 19, 2018
BACKGROUND: Application of deep-learning technology to skin cancer classification can potentially improve the sensitivity and specificity of skin cancer screening, but the number of training images required for such a system is thought to be extremel...
Mobile Applications Skin Dermatologists Image Interpretation, Computer-Assisted Datasets as Topic Sensitivity and Specificity Deep Learning Smartphone Humans Skin Neoplasms Dermoscopy
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Performance and clinical impact of machine learning based lung nodule detection using vessel suppression in melanoma patients.

Clinical imaging Sep 11, 2018
PURPOSE: To evaluate performance and the clinical impact of a novel machine learning based vessel-suppressing computer-aided detection (CAD) software in chest computed tomography (CT) of patients with malignant melanoma.
Aged, 80 and over Adult Male Lung Neoplasms Retrospective Studies Middle Aged Humans Neoplasm Metastasis Melanoma Melanoma, Cutaneous Malignant Female Diagnosis, Computer-Assisted Multidetector Computed Tomography Machine Learning Skin Neoplasms ROC Curve Aged
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Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features.

IEEE transactions on bio-medical engineering Aug 20, 2018
In this paper, we present a novel framework for dermoscopy image recognition via both a deep learning method and a local descriptor encoding strategy. Specifically, deep representations of a rescaled dermoscopy image are first extracted via a very de...
Deep Learning Support Vector Machine Melanoma Image Interpretation, Computer-Assisted Humans Skin Neoplasms Algorithms Dermoscopy Databases, Factual
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