AIMC Topic: Skin Neoplasms
Clear Filters Showing 261 to 270 of 485 articles
Deep-learning approach in the study of skin lesions.
Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
Apr 6, 2021
BACKGROUND: Medical technology is far from reaching its full potential. An area that is currently expanding is that of precision medicine. The aim of this article is to present an application of precision medicine-a deep-learning approach to computer...
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.
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...
AI-based smartphone apps for risk assessment of skin cancer need more evaluation and better regulation.
British journal of cancer
Mar 19, 2021
Smartphone applications ("apps") with artificial intelligence (AI) algorithms are increasingly used in healthcare. Widespread adoption of these apps must be supported by a robust evidence-base and app manufacturers' claims appropriately regulated. Cu...
Europe
United States Food and Drug Administration
Precancerous Conditions
Evidence-Based Medicine
European Union
Humans
Diagnostic Test Approval
Skin Neoplasms
Mobile Applications
Adult
United States
Artificial Intelligence
Early Detection of Cancer
Sensitivity and Specificity
Algorithms
Risk Assessment
Smartphone
Whole-slide margin control through deep learning in Mohs micrographic surgery for basal cell carcinoma.
Experimental dermatology
Mar 3, 2021
BACKGROUND: Basal cell carcinoma (BCC) is the most common type of skin cancer with incidence rates rising each year. Mohs micrographic surgery (MMS) is most often chosen as treatment for BCC on the face for which each frozen section has to be histolo...
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
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...
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...
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...
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.
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 31059
- Neural Networks, Computer 29727
- Algorithms 26848
- Deep Learning 26366
- Artificial Intelligence 24311
- Robotics 13314
- Image Processing, Computer-Assisted 9527
- Retrospective Studies 9114
- Magnetic Resonance Imaging 6186
- Reproducibility of Results 5593
- Tomography, X-Ray Computed 4772
- Support Vector Machine 4688
- Computational Biology 4240
- Brain 4053
- Computer Simulation 3882
Recent Journals
- Scientific reports 5971
- Sensors (Basel, Switzerland) 4794
- PloS one 4022
- Neural networks : the official journal of the International Neural Network Society 2930
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1860
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1131
- Journal of chemical information and modeling 1000
- Nature communications 949
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 853
- IEEE transactions on neural networks and learning systems 826