AIMC Topic: Melanoma

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Deep learning approach for skin melanoma and benign classification using empirical wavelet decomposition.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2024
BACKGROUND: Melanoma is a malignant skin cancer that causes high mortality. Early detection of melanoma can save patients' lives. The features of the skin lesion images can be extracted using computer techniques to differentiate early between melanom...
Skin Neoplasms Humans Melanoma Deep Learning Wavelet Analysis
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[MOCK MOLE: PRODUCING SYNTHETIC IMAGES THAT RECAPITULATE CONFOCAL PATTERNS OF MELANOCYTIC NEVI VIA DEEP-LEARNING MODELS].

Harefuah Dec 1, 2023
INTRODUCTION: Melanocytic nevi present microscopic patterns, which differ in their associated melanoma risk, and can be non-invasively recognized under Reflectance Confocal Microscopy (RCM).
Microscopy, Confocal Image Interpretation, Computer-Assisted Nevus, Pigmented Skin Neoplasms Deep Learning Humans Melanoma
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Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things.

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) Nov 1, 2023
INTRODUCTION: Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and c...
Deep Learning Internet Melanoma Skin Neoplasms Skin Diseases Dermoscopy Humans
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Unveiling the power of convolutional neural networks in melanoma diagnosis.

European journal of dermatology : EJD Oct 1, 2023
Convolutional neural networks are a type of deep learning algorithm. They are mostly applied in visual recognition and can be used for the identification of melanomas. Multiple studies have evaluated the performance of convolutional neural networks, ...
Melanoma Skin Neoplasms Dermatologists Dermoscopy Artificial Intelligence Humans Algorithms Neural Networks, Computer
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[Deep learning-based fully automated intelligent and precise diagnosis for melanocytic lesions].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Oct 25, 2022
Melanocytic lesions occur on the surface of the skin, in which the malignant type is melanoma with a high fatality rate, seriously endangering human health. The histopathological analysis is the gold standard for diagnosis of melanocytic lesions. In ...
Melanoma Diagnosis, Computer-Assisted Deep Learning Humans Neural Networks, Computer Skin
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Machine learning for the identification of decision boundaries during the transition from radial to vertical growth phase superficial spreading melanomas.

Melanoma research Dec 1, 2021
The objective of this study was to compute threshold values for the diameter of superficial spreading melanomas (SSMs) at which the radial growth phase (RGP) evolves into an invasive vertical growth phase (VGP). We examined reports from 1995 to 2019 ...
Skin Neoplasms Male Middle Aged Female Melanoma Machine Learning Retrospective Studies Humans
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Novel strategy for applying hierarchical density-based spatial clustering of applications with noise towards spectroscopic analysis and detection of melanocytic lesions.

Melanoma research Dec 1, 2021
Advancements in dermoscopy techniques have elucidated identifiable characteristics of melanoma which revolve around the asymmetrical constitution of melanocytic lesions consequent of unfettered proliferative growth as a malignant lesion. This study e...
Cluster Analysis Magnetic Resonance Spectroscopy Melanocytes Melanoma Skin Neoplasms Humans Machine Learning
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Melanoma Skin Cancer Detection Using Recent Deep Learning Models.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Melanoma is considered as one of the world's deadly cancers. This type of skin cancer will spread to other areas of the body if not detected at an early stage. Convolutional Neural Network (CNN) based classifiers are currently considered one of the m...
Neural Networks, Computer Melanoma Dermoscopy Deep Learning Skin Neoplasms Humans
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A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma.

Journal for immunotherapy of cancer Nov 1, 2021
BACKGROUND: To assess the additive value of dual-energy CT (DECT) over single-energy CT (SECT) to radiomics-based response prediction in patients with metastatic melanoma preceding immunotherapy.
Aged Middle Aged Tomography, X-Ray Computed Radiometry Female Humans Neoplasm Staging Melanoma Machine Learning
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XGSEA: CROSS-species gene set enrichment analysis via domain adaptation.

Briefings in bioinformatics Sep 2, 2021
MOTIVATION: Gene set enrichment analysis (GSEA) has been widely used to identify gene sets with statistically significant difference between cases and controls against a large gene set. GSEA needs both phenotype labels and expression of genes. Howeve...
Embryo, Mammalian Melanoma Datasets as Topic Skin Neoplasms Ovarian Neoplasms Humans Brain Neoplasms Mice Zebrafish Machine Learning Computational Biology Animals Female Gene Expression Regulation, Neoplastic T-Lymphocytes
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