AIMC Topic: Dermoscopy

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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 Algorithms Deep Learning Bayes Theorem Skin Diseases Melanoma Skin Neoplasms Dermoscopy
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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.
Cross-Sectional Studies Diagnosis, Computer-Assisted Deep Learning Skin Neoplasms Dermoscopy Reproducibility of Results Nevus Predictive Value of Tests Image Interpretation, Computer-Assisted Artifacts Humans Retrospective Studies Melanoma
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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...
Humans Neural Networks, Computer Reproducibility of Results Diagnosis, Computer-Assisted Diagnosis, Differential Predictive Value of Tests Image Interpretation, Computer-Assisted Nevus Melanoma Skin Neoplasms Dermoscopy
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Melanoma diagnosis using deep learning techniques on dermatoscopic images.

BMC medical imaging Jan 6, 2021
BACKGROUND: Melanoma has become more widespread over the past 30 years and early detection is a major factor in reducing mortality rates associated with this type of skin cancer. Therefore, having access to an automatic, reliable system that is able ...
Image Interpretation, Computer-Assisted Dermoscopy Deep Learning ROC Curve Humans Melanoma Skin Neoplasms
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A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi.

Journal of dermatological science Dec 2, 2020
BACKGROUND: Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists' experience and fails to achieve adequate...
Image Interpretation, Computer-Assisted Datasets as Topic Nevus Child Diagnosis, Differential Skin Deep Learning Adolescent Child, Preschool Humans Retrospective Studies Female Aged, 80 and over Dermoscopy Infant, Newborn Young Adult Male Reproducibility of Results Melanoma Adult Aged Middle Aged Skin Neoplasms Infant
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Deep learning based classification of facial dermatological disorders.

Computers in biology and medicine Nov 13, 2020
Common properties of dermatological diseases are mostly lesions with abnormal pattern and skin color (usually redness). Therefore, dermatology is one of the most appropriate areas in medicine for automated diagnosis from images using pattern recognit...
Humans Dermoscopy Melanoma Skin Neoplasms Deep Learning Skin Diseases
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Artificial intelligence for melanoma diagnosis.

Italian journal of dermatology and venereology Nov 12, 2020
Convolutional neural networks (CNN) have shown unprecedented accuracy in digital image analysis, which can be harnessed for melanoma recognition through automated evaluation of clinical and dermatoscopic images. In experimental studies, modern CNN ar...
Prospective Studies Melanoma Dermoscopy Humans Neural Networks, Computer Artificial Intelligence
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Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities.

Computers in biology and medicine Oct 27, 2020
Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasing incidence of skin cancers, low awareness among a growing population,...
Diagnosis, Computer-Assisted Algorithms Skin Neoplasms Humans Dermoscopy Artificial Intelligence
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Dermoscopic diagnostic performance of Japanese dermatologists for skin tumors differs by patient origin: A deep learning convolutional neural network closes the gap.

The Journal of dermatology Oct 15, 2020
In the dermoscopic diagnosis of skin tumors, it remains unclear whether a deep neural network (DNN) trained with images from fair-skinned-predominant archives is helpful when applied for patients with darker skin. This study compared the performance ...
Neural Networks, Computer Skin Neoplasms Dermoscopy Humans Deep Learning Dermatologists Japan
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Effective Melanoma Recognition Using Deep Convolutional Neural Network with Covariance Discriminant Loss.

Sensors (Basel, Switzerland) Oct 13, 2020
Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discrim...
Dermoscopy Deep Learning Humans Algorithms Neural Networks, Computer Melanoma Skin Neoplasms
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