AIMC Topic: Skin Neoplasms

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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 Humans Artificial Intelligence Algorithms Skin Neoplasms Dermoscopy
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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 ...
Deep Learning Dermatologists Japan Humans Neural Networks, Computer Skin Neoplasms Dermoscopy
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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 Algorithms Melanoma Skin Neoplasms Humans Neural Networks, Computer
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Performance of a deep learning-based application for the diagnosis of basal cell carcinoma in Indian patients as compared to dermatologists and nondermatologists.

International journal of dermatology Oct 11, 2020
Humans Skin Neoplasms Carcinoma, Basal Cell Deep Learning Dermatologists Dermatology
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Optimization of an automated tumor-infiltrating lymphocyte algorithm for improved prognostication in primary melanoma.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Oct 1, 2020
Tumor-infiltrating lymphocytes (TIL) have potential prognostic value in melanoma and have been considered for inclusion in the American Joint Committee on Cancer (AJCC) staging criteria. However, interobserver discordance continues to prevent the ado...
Humans Machine Learning Automation, Laboratory Lymphocytes, Tumor-Infiltrating Predictive Value of Tests Neoplasm Staging Risk Assessment Melanoma Risk Factors Disease Progression Diagnosis, Computer-Assisted Skin Neoplasms Aged Reproducibility of Results Microscopy Databases, Factual Adult Tumor Microenvironment Biopsy Retrospective Studies Image Interpretation, Computer-Assisted Male Neoplasm Recurrence, Local Female Lymphocyte Count Middle Aged Progression-Free Survival
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Review of medical image recognition technologies to detect melanomas using neural networks.

BMC bioinformatics Sep 14, 2020
BACKGROUND: Melanoma is one of the most aggressive types of cancer that has become a world-class problem. According to the World Health Organization estimates, 132,000 cases of the disease and 66,000 deaths from malignant melanoma and other forms of ...
Early Detection of Cancer Melanoma, Cutaneous Malignant Data Accuracy Image Interpretation, Computer-Assisted Skin Neoplasms Sensitivity and Specificity Deep Learning Humans Melanoma
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Clinically Relevant Vulnerabilities of Deep Machine Learning Systems for Skin Cancer Diagnosis.

The Journal of investigative dermatology Sep 12, 2020
Color Skin Nevus, Pigmented Dermoscopy Image Interpretation, Computer-Assisted Humans Deep Learning Melanoma Diagnosis, Differential Skin Neoplasms
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A deep learning algorithm with high sensitivity for the detection of basal cell carcinoma in Mohs micrographic surgery frozen sections.

Journal of the American Academy of Dermatology Sep 11, 2020
Mohs Surgery Carcinoma, Basal Cell Deep Learning Frozen Sections Humans Skin Neoplasms
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Diagnosis of congenital pigmented macules in infants with reflectance confocal microscopy and machine learning.

Journal of the American Academy of Dermatology Sep 11, 2020
Humans Hutchinson's Melanotic Freckle Machine Learning Microscopy, Confocal Skin Neoplasms Diagnosis, Differential Pigmentation Disorders Dermoscopy Infant
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Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study.

Journal of medical Internet research Sep 11, 2020
BACKGROUND: Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence (AI) in classifying dermoscopic images of melanoma and nevi, concluding that th...
Humans Dermatologists Artificial Intelligence Surveys and Questionnaires Melanoma Diagnostic Imaging Skin Neoplasms Internet Dermoscopy
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