AIMC Topic: Skin Neoplasms

Clear Filters Showing 81 to 90 of 485 articles

Leveraging AI and patient metadata to develop a novel risk score for skin cancer detection.

Scientific reports
Melanoma of the skin is the 17th most common cancer worldwide. Early detection of suspicious skin lesions (melanoma) can increase 5-year survival rates by 20%. The 7-point checklist (7PCL) has been extensively used to suggest urgent referrals for pat...

Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets.

Scientific reports
Skin cancer (SC) is an important medical condition that necessitates prompt identification to ensure timely treatment. Although visual evaluation by dermatologists is considered the most reliable method, its efficacy is subjective and laborious. Deep...

Weakly supervised deep learning image analysis can differentiate melanoma from naevi on haematoxylin and eosin-stained histopathology slides.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: The broad histomorphological spectrum of melanocytic pathologies requires large data sets to develop accurate and generalisable deep learning (DL)-based diagnostic pathology classifiers. Weakly supervised DL promotes utilisation of larger...

Computer-aided diagnosis of eyelid skin tumors using machine learning.

Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
OBJECTIVE: To develop an automated, new framework based on machine learning to diagnose malignant eyelid skin tumors.

Radiomic and deep learning analysis of dermoscopic images for skin lesion pattern decoding.

Scientific reports
This study aims to explore the efficacy of a hybrid deep learning and radiomics approach, supplemented with patient metadata, in the noninvasive dermoscopic imaging-based diagnosis of skin lesions. We analyzed dermoscopic images from the Internationa...

Computerizing the first step of the two-step algorithm in dermoscopy: A convolutional neural network for differentiating melanocytic from non-melanocytic skin lesions.

European journal of cancer (Oxford, England : 1990)
IMPORTANCE: Convolutional neural networks (CNN) have shown performance equal to trained dermatologists in differentiating benign from malignant skin lesions. To improve clinicians' management decisions, additional classifications into diagnostic cate...

EAAC-Net: An Efficient Adaptive Attention and Convolution Fusion Network for Skin Lesion Segmentation.

Journal of imaging informatics in medicine
Accurate segmentation of skin lesions in dermoscopic images is of key importance for quantitative analysis of melanoma. Although existing medical image segmentation methods significantly improve skin lesion segmentation, they still have limitations i...

A Novel Artificial Intelligence-Based Parameterization Approach of the Stromal Landscape in Merkel Cell Carcinoma: A Multi-Institutional Study.

Laboratory investigation; a journal of technical methods and pathology
Tumor-stroma ratio (TSR) has been recognized as a valuable prognostic indicator in various solid tumors. This study aimed to examine the clinicopathologic relevance of TSR in Merkel cell carcinoma (MCC) using artificial intelligence (AI)-based parame...