AIMC Topic: Carcinoma, Basal Cell

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Artificial intelligence for automatic detection of basal cell carcinoma from frozen tissue tangential biopsies.

Clinical and experimental dermatology Jun 25, 2024
Evaluation of basal cell carcinoma (BCC) involves tangential biopsies of a suspicious lesion that is sent for frozen sections and evaluated by a Mohs micrographic surgeon. Advances in artificial intelligence (AI) have made possible the development of...
Skin Neoplasms Carcinoma, Basal Cell Humans Artificial Intelligence Frozen Sections Mohs Surgery Sensitivity and Specificity Biopsy
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Differentiation and risk stratification of basal cell carcinoma with deep learning on histopathologic images and measuring nuclei and tumor microenvironment features.

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) Jan 1, 2024
BACKGROUND: Nuclear pleomorphism and tumor microenvironment (TME) play a critical role in cancer development and progression. Identifying most predictive nuclei and TME features of basal cell carcinoma (BCC) may provide insights into which characteri...
Carcinoma, Basal Cell Tumor Microenvironment Risk Assessment Deep Learning Humans Skin Neoplasms
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Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma.

Journal of biomedical optics Jun 1, 2022
SIGNIFICANCE: Raman spectroscopy (RS) provides an automated approach for assisting Mohs micrographic surgery for skin cancer diagnosis; however, the specificity of RS is limited by the high spectral similarity between tumors and normal tissues struct...
Humans Dermoscopy Deep Learning Skin Neoplasms Carcinoma, Basal Cell Microscopy, Confocal
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Deep learning with transfer learning in pathology. Case study: classification of basal cell carcinoma.

Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologie Jan 1, 2021
Establishing basal cell carcinoma (BCC) subtype is sometimes challenging for pathologists. Deep-learning (DL) algorithms are an emerging approach in image classification due to their performance, accompanied by a new concept - transfer learning, whic...
Pathologists Deep Learning Humans Algorithms Skin Neoplasms Carcinoma, Basal Cell
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Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network.

JAMA dermatology Jan 1, 2020
IMPORTANCE: Detection of cutaneous cancer on the face using deep-learning algorithms has been challenging because various anatomic structures create curves and shades that confuse the algorithm and can potentially lead to false-positive results.
Face Photography Datasets as Topic Female Skin Neoplasms ROC Curve Carcinoma, Basal Cell Male Image Processing, Computer-Assisted Carcinoma, Squamous Cell Humans Middle Aged Skin Neural Networks, Computer Aged Keratinocytes Adult
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[Clinical image identification of basal cell carcinoma and pigmented nevi based on convolutional neural network].

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences Sep 28, 2019
To construct an intelligent assistant diagnosis model based on the clinical images of basal cell carcinoma (BCC) and pigmented nevi in Chinese by using the advanced convolutional neural network (CNN).
 Methods: Based on the Xiangya Medical Big Data P...
Humans Nevus, Pigmented Carcinoma, Basal Cell Area Under Curve Neural Networks, Computer Skin Neoplasms
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Convolutional Neural Network Approach to Classify Skin Lesions Using Reflectance Confocal Microscopy.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
We propose an approach based on a convolutional neural network to classify skin lesions using the reflectance confocal microscopy (RCM) mosaics. Skin cancers are the most common type of cancers and a correct, early diagnosis significantly lowers both...
Nevus, Pigmented Skin Neoplasms Microscopy, Confocal Carcinoma, Basal Cell Dermoscopy Humans Neural Networks, Computer Sensitivity and Specificity Melanoma
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