AIMC Topic: Dermoscopy

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Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.

European journal of cancer (Oxford, England : 1990) Sep 8, 2021
BACKGROUND: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinic...
Melanoma Skin Neoplasms Dermoscopy Reproducibility of Results Humans Neural Networks, Computer Dermatologists Microscopy Image Interpretation, Computer-Assisted Automation Predictive Value of Tests Biopsy Pathologists Diagnosis, Computer-Assisted Deep Learning Clinical Competence
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An Attention-Based Mechanism to Combine Images and Metadata in Deep Learning Models Applied to Skin Cancer Classification.

IEEE journal of biomedical and health informatics Sep 3, 2021
Computer-aided skin cancer classification systems built with deep neural networks usually yield predictions based only on images of skin lesions. Despite presenting promising results, it is possible to achieve higher performance by taking into accoun...
Skin Neoplasms Dermoscopy Deep Learning Metadata Humans Neural Networks, Computer
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AI outperformed every dermatologist in dermoscopic melanoma diagnosis, using an optimized deep-CNN architecture with custom mini-batch logic and loss function.

Scientific reports Sep 1, 2021
Melanoma, one of the most dangerous types of skin cancer, results in a very high mortality rate. Early detection and resection are two key points for a successful cure. Recent researches have used artificial intelligence to classify melanoma and nevu...
Dermoscopy Deep Learning ROC Curve Algorithms Melanoma Neural Networks, Computer Skin Neoplasms Artificial Intelligence Humans Dermatologists
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Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering.

Microscopy research and technique Aug 27, 2021
Melanoma skin cancer is the most life-threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effective...
Skin Neoplasms Dermoscopy Deep Learning Humans Algorithms Melanoma Cluster Analysis
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Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy.

The Journal of investigative dermatology Jul 13, 2021
Basal cell carcinoma (BCC) is the most common skin cancer, with over 2 million cases diagnosed annually in the United States. Conventionally, BCC is diagnosed by naked eye examination and dermoscopy. Suspicious lesions are either removed or biopsied ...
Deep Learning Microscopy, Confocal Biopsy Models, Biological Automation Physical Examination Adult Humans Male Artificial Intelligence Middle Aged Carcinoma, Basal Cell Reproducibility of Results Dermoscopy Female Aged Skin Neoplasms Aged, 80 and over
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Predicting the clinical management of skin lesions using deep learning.

Scientific reports Apr 8, 2021
Automated machine learning approaches to skin lesion diagnosis from images are approaching dermatologist-level performance. However, current machine learning approaches that suggest management decisions rely on predicting the underlying skin conditio...
Deep Learning Image Interpretation, Computer-Assisted Humans Skin Dermoscopy Skin Diseases
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Comparison of performance of a deep learning-based mobile application with non-dermatologist physicians in the diagnosis of common skin diseases.

International journal of dermatology Mar 2, 2021
Humans Physicians Deep Learning Mobile Applications Skin Neoplasms Dermoscopy Skin Diseases
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A hierarchical three-step superpixels and deep learning framework for skin lesion classification.

Methods (San Diego, Calif.) Feb 19, 2021
Skin cancer is one of the most common and dangerous cancer that exists worldwide. Malignant melanoma is one of the most dangerous skin cancer types has a high mortality rate. An estimated 196,060 melanoma cases will be diagnosed in 2020 in the USA. M...
Humans Skin Diseases Dermoscopy Deep Learning Bayes Theorem Algorithms Melanoma Skin Neoplasms
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Association between different scale bars in dermoscopic images and diagnostic performance of a market-approved deep learning convolutional neural network for melanoma recognition.

European journal of cancer (Oxford, England : 1990) Jan 16, 2021
BACKGROUND: Studies systematically unravelling possible causes for false diagnoses of deep learning convolutional neural networks (CNNs) are scarce, yet needed before broader application.
Nevus Predictive Value of Tests Cross-Sectional Studies Humans Skin Neoplasms Image Interpretation, Computer-Assisted Diagnosis, Computer-Assisted Retrospective Studies Dermoscopy Artifacts Deep Learning Melanoma Reproducibility of Results
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Robustness of convolutional neural networks in recognition of pigmented skin lesions.

European journal of cancer (Oxford, England : 1990) Jan 7, 2021
BACKGROUND: A basic requirement for artificial intelligence (AI)-based image analysis systems, which are to be integrated into clinical practice, is a high robustness. Minor changes in how those images are acquired, for example, during routine skin c...
Melanoma Skin Neoplasms Dermoscopy Humans Nevus Reproducibility of Results Predictive Value of Tests Neural Networks, Computer Diagnosis, Computer-Assisted Image Interpretation, Computer-Assisted Diagnosis, Differential
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