AIMC Topic: Skin Neoplasms

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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...

Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.

Journal of imaging informatics in medicine
Deep learning techniques offer improvements in computer-aided diagnosis systems. However, acquiring image domain annotations is challenging due to the knowledge and commitment required of expert pathologists. Pathologists often identify regions in wh...

The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation.

International journal of dermatology
BACKGROUND: Artificial intelligence (AI) and large language models (LLMs) transform how patients inform themselves. LLMs offer potential as educational tools, but their quality depends upon the information generated. Current literature examining AI a...

Artificial intelligence-assisted metastasis and prognosis model for patients with nodular melanoma.

PloS one
OBJECTIVE: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithm...

Utilizing deep learning model for assessing melanocytic density in resection margins of lentigo maligna.

Diagnostic pathology
BACKGROUND: Surgical excision with clear histopathological margins is the preferred treatment to prevent progression of lentigo maligna (LM) to invasive melanoma. However, the assessment of resection margins on sun-damaged skin is challenging. We dev...