AIMC Topic: Melanoma

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Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification.

European journal of cancer (Oxford, England : 1990) Apr 7, 2021
BACKGROUND: Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses.
Aged Adult Male Middle Aged Melanoma Skin Neoplasms Reproducibility of Results Sex Factors Image Interpretation, Computer-Assisted Germany Nevus Age Factors Predictive Value of Tests Microscopy Humans Retrospective Studies Female Neural Networks, Computer Databases, Factual
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Adopting low-shot deep learning for the detection of conjunctival melanoma using ocular surface images.

Computer methods and programs in biomedicine Apr 3, 2021
BACKGROUND AND OBJECTIVE: The purpose of the present study was to investigate low-shot deep learning models applied to conjunctival melanoma detection using a small dataset with ocular surface images.
Deep Learning Nevus, Pigmented Neural Networks, Computer Melanoma Skin Neoplasms Humans
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Ultra-high-frequency ultrasound and machine learning approaches for the differential diagnosis of melanocytic lesions.

Experimental dermatology Mar 31, 2021
Malignant melanoma (MM) is one of the most dangerous skin cancers. The aim of this study was to present a potential new method for the differential diagnosis of MM from melanocytic naevi (MN). We examined 20 MM and 19 MN with a new ultra-high-frequen...
Machine Learning Female Melanoma Humans Adult Sensitivity and Specificity Male Middle Aged Skin Neoplasms Aged Aged, 80 and over Ultrasonography Melanoma, Cutaneous Malignant
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Pushing the boundaries of computer-aided diagnosis of melanoma.

The Lancet. Oncology Feb 25, 2021
Melanoma Humans Diagnosis, Computer-Assisted Neural Networks, Computer
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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...
Bayes Theorem Skin Diseases Skin Neoplasms Dermoscopy Deep Learning Humans Algorithms Melanoma
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Automated Detection and Segmentation of Brain Metastases in Malignant Melanoma: Evaluation of a Dedicated Deep Learning Model.

AJNR. American journal of neuroradiology Feb 4, 2021
BACKGROUND AND PURPOSE: Malignant melanoma is an aggressive skin cancer in which brain metastases are common. Our aim was to establish and evaluate a deep learning model for fully automated detection and segmentation of brain metastases in patients w...
Magnetic Resonance Imaging Automation Humans Brain Neoplasms Deep Learning Melanoma Skin Neoplasms
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Artificial intelligence in dermatopathology: Diagnosis, education, and research.

Journal of cutaneous pathology Jan 26, 2021
Artificial intelligence (AI) utilizes computer algorithms to carry out tasks with human-like intelligence. Convolutional neural networks, a type of deep learning AI, can classify basal cell carcinoma, seborrheic keratosis, and conventional nevi, high...
Carcinoma, Basal Cell Deep Learning Diagnosis, Differential Observer Variation Pathology Humans Neural Networks, Computer Dermatology Diagnostic Tests, Routine Research Artificial Intelligence Melanoma Machine Learning Skin Neoplasms Algorithms Nevus Keratosis, Seborrheic
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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 Skin Neoplasms Diagnosis, Computer-Assisted Dermoscopy Humans Predictive Value of Tests Deep Learning Reproducibility of Results Artifacts Retrospective Studies Image Interpretation, Computer-Assisted Nevus 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...
Skin Neoplasms Dermoscopy Reproducibility of Results Humans Neural Networks, Computer Melanoma Image Interpretation, Computer-Assisted Nevus Diagnosis, Computer-Assisted Diagnosis, Differential Predictive Value of Tests
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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 ...
Deep Learning Melanoma ROC Curve Image Interpretation, Computer-Assisted Humans Skin Neoplasms Dermoscopy
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