AIMC Topic: Melanoma

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Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.

European journal of cancer (Oxford, England : 1990) Jul 18, 2019
BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25-2...
Humans Melanoma Skin Neoplasms Reproducibility of Results Diagnosis, Computer-Assisted Deep Learning Observer Variation Pathologists Diagnosis, Differential Nevus Microscopy Predictive Value of Tests Image Interpretation, Computer-Assisted Biopsy
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Automated identification of malignancy in whole-slide pathological images: identification of eyelid malignant melanoma in gigapixel pathological slides using deep learning.

The British journal of ophthalmology Jul 13, 2019
BACKGROUND/AIMS: To develop a deep learning system (DLS) that can automatically detect malignant melanoma (MM) in the eyelid from histopathological sections with colossal information density.
Artificial Intelligence Algorithms Retrospective Studies Humans Eyelids Eyelid Neoplasms Melanoma Deep Learning ROC Curve
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The Application of Deep Learning in the Risk Grading of Skin Tumors for Patients Using Clinical Images.

Journal of medical systems Jul 13, 2019
According to diagnostic criteria, skin tumors can be divided into three categories: benign, low degree and high degree malignancy. For high degree malignant skin tumors, if not detected in time, they can do serious harm to patients' health. However, ...
Humans Melanoma, Cutaneous Malignant Skin Neoplasms Patient Participation Deep Learning Neural Networks, Computer Cell Phone Area Under Curve Databases, Factual Melanoma
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Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017.

Journal of the American Academy of Dermatology Jul 12, 2019
BACKGROUND: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain.
Nevus Keratosis, Seborrheic Israel Colombia Image Interpretation, Computer-Assisted Spain International Cooperation Internship and Residency Humans Dermoscopy Cross-Sectional Studies Diagnosis, Differential Melanoma Deep Learning Skin Skin Neoplasms ROC Curve Dermatologists United States
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Melanoma Detection by Means of Multiple Instance Learning.

Interdisciplinary sciences, computational life sciences Jul 10, 2019
We present an application to melanoma detection of a multiple instance learning (MIL) approach, whose objective, in the binary case, is to discriminate between positive and negative sets of items. In the MIL terminology these sets are called bags and...
Humans Artificial Intelligence Databases, Factual Algorithms Melanoma Support Vector Machine
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ASO Author Reflections: Robotic Pelvic Lymph Node Dissection for Metastatic Melanoma-A Minimally Invasive Approach to a Difficult Problem.

Annals of surgical oncology Jul 8, 2019
Humans Melanoma Lymph Node Excision Robotics Robotic Surgical Procedures
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Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers.

Journal of medical systems Jul 4, 2019
Most common and deadly type of cancer is Skin cancer. The destructive kind of cancers in skin is Melanoma as well as it can be identified at the initial stage and can be cured completely. For the diagnosis of melanoma, the identification of the melan...
Algorithms Humans Skin Neoplasms Support Vector Machine Melanoma
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Assessing the effectiveness of artificial intelligence methods for melanoma: A retrospective review.

Journal of the American Academy of Dermatology Jun 27, 2019
BACKGROUND: Artificial intelligence methods for the classification of melanoma have been studied extensively. However, few studies compare these methods under the same standards.
Humans Artificial Intelligence Dermoscopy Reproducibility of Results Sensitivity and Specificity Retrospective Studies Melanoma Skin Neoplasms
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Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions.

PloS one Jun 24, 2019
BACKGROUND: In recent months, multiple publications have demonstrated the use of convolutional neural networks (CNN) to classify images of skin cancer as precisely as dermatologists. However, these CNNs failed to outperform the International Symposiu...
Dermoscopy Reproducibility of Results Skin Databases, Factual Deep Learning Dermatologists Humans Melanoma Image Interpretation, Computer-Assisted Neural Networks, Computer Skin Neoplasms Algorithms
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Artificial intelligence in dermato-oncology: a joint clinical and data science perspective.

International journal of dermatology May 31, 2019
Deep Learning Data Science Early Detection of Cancer Datasets as Topic Dermatology Humans Image Interpretation, Computer-Assisted Melanoma Medical Oncology Skin Neoplasms
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