AIMC Topic: Melanoma, Cutaneous Malignant
Clear Filters Showing 31 to 37 of 37 articles
The Application of Deep Learning in the Risk Grading of Skin Tumors for Patients Using Clinical Images.
Journal of medical systems
Jul 13, 2019
According to diagnostic criteria, skin tumors can be divided into three categories: benign, low degree and high degree malignancy. For high degree malignant skin tumors, if not detected in time, they can do serious harm to patients' health. However, ...
Performance and clinical impact of machine learning based lung nodule detection using vessel suppression in melanoma patients.
Clinical imaging
Sep 11, 2018
PURPOSE: To evaluate performance and the clinical impact of a novel machine learning based vessel-suppressing computer-aided detection (CAD) software in chest computed tomography (CT) of patients with malignant melanoma.
Melanoma segmentation based on deep learning.
Computer assisted surgery (Abingdon, England)
Oct 18, 2017
Malignant melanoma is one of the most deadly forms of skin cancer, which is one of the world's fastest-growing cancers. Early diagnosis and treatment is critical. In this study, a neural network structure is utilized to construct a broad and accurate...
[Research advances in the pathological diagnosis of melanocytic tumors with artificial intelligence].
Zhonghua bing li xue za zhi = Chinese journal of pathology
Jan 8, 2025
Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.
Journal of cellular and molecular medicine
Jul 1, 2024
Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogeneity, yet research on this aspect in cutaneous melanoma remains limited. In this study, we utilized single-cell data from 92,521 cells to explore the tu...
Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism.
Experimental dermatology
Jun 1, 2024
This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE2...
Population-Based Analysis of Histologically Confirmed Melanocytic Proliferations Using Natural Language Processing.
JAMA dermatology
Jan 1, 2018
IMPORTANCE: Population-based information on the distribution of histologic diagnoses associated with skin biopsies is unknown. Electronic medical records (EMRs) enable automated extraction of pathology report data to improve our epidemiologic underst...
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