AIMC Topic: Melanocytes
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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)
Aug 25, 2024
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...
Utilizing deep learning model for assessing melanocytic density in resection margins of lentigo maligna.
Diagnostic pathology
Aug 3, 2024
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...
An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma.
Scientific reports
Dec 14, 2023
When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherite...
A machine learning algorithm for simulating immunohistochemistry: development of SOX10 virtual IHC and evaluation on primarily melanocytic neoplasms.
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Apr 1, 2020
Immunohistochemistry (IHC) is a diagnostic technique used throughout pathology. A machine learning algorithm that could predict individual cell immunophenotype based on hematoxylin and eosin (H&E) staining would save money, time, and reduce tissue co...
Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders.
Sensors (Basel, Switzerland)
Mar 11, 2020
In this research, we present a semi-supervised segmentation solution using convolutional autoencoders to solve the problem of segmentation tasks having a small number of ground-truth images. We evaluate the proposed deep network architecture for the ...
Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas.
Journal of the European Academy of Dermatology and Venereology : JEADV
Jan 21, 2020
BACKGROUND: Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not be...
Skin lesion classification with ensembles of deep convolutional neural networks.
Journal of biomedical informatics
Aug 10, 2018
Skin cancer is a major public health problem with over 123,000 newly diagnosed cases worldwide in every year. Melanoma is the deadliest form of skin cancer, responsible for over 9000 deaths in the United States each year. Thus, reliable automatic mel...
Deep Tissue Sequencing Using Hypodermoscopy and Augmented Intelligence to Analyze Atypical Pigmented Lesions.
Journal of cutaneous medicine and surgery
Aug 1, 2018
BACKGROUND: Over the past decade, new technologies, devices, and methods have been developed to assist in the diagnosis of cutaneous melanocytic lesions.
[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
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...
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