AIMC Topic: Skin Neoplasms
Clear Filters Showing 341 to 350 of 486 articles
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...
Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.
The Lancet. Oncology
Jun 12, 2019
BACKGROUND: Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human...
Artificial intelligence in dermato-oncology: a joint clinical and data science perspective.
International journal of dermatology
May 31, 2019
Pathologist-level classification of histopathological melanoma images with deep neural networks.
European journal of cancer (Oxford, England : 1990)
May 23, 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 25-26% ...
The role of AI classifiers in skin cancer images.
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)
May 20, 2019
BACKGROUND: The use of different imaging modalities to assist in skin cancer diagnosis is a common practice in clinical scenarios. Different features representative of the lesion under evaluation can be retrieved from image analysis and processing. H...
Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture.
Computer methods and programs in biomedicine
May 15, 2019
BACKGROUND AND OBJECTIVE: Skin melanoma is one of the major health problems in many countries. Dermatologists usually diagnose melanoma by visual inspection of moles. Digital hair removal can provide a non-invasive way to remove hair and hair-like re...
Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope.
EBioMedicine
May 14, 2019
BACKGROUND: Skin cancer (SC), especially melanoma, is a growing public health burden. Experimental studies have indicated a potential diagnostic role for deep learning (DL) algorithms in identifying SC at varying sensitivities. Previously, it was dem...
Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting.
IEEE transactions on bio-medical engineering
May 9, 2019
OBJECTIVE: This paper addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification. The second problem is the high-class imb...
A comparative study of deep learning architectures on melanoma detection.
Tissue & cell
Apr 22, 2019
Melanoma is the most aggressive type of skin cancer, which significantly reduces the life expectancy. Early detection of melanoma can reduce the morbidity and mortality associated with skin cancer. Dermoscopic images acquired by dermoscopic instrumen...
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