AIMC Topic: Skin Neoplasms

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Optimization of an automated tumor-infiltrating lymphocyte algorithm for improved prognostication in primary melanoma.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Oct 1, 2020
Tumor-infiltrating lymphocytes (TIL) have potential prognostic value in melanoma and have been considered for inclusion in the American Joint Committee on Cancer (AJCC) staging criteria. However, interobserver discordance continues to prevent the ado...
Disease Progression Diagnosis, Computer-Assisted Lymphocytes, Tumor-Infiltrating Predictive Value of Tests Male Melanoma Middle Aged Skin Neoplasms Risk Assessment Reproducibility of Results Progression-Free Survival Adult Neoplasm Staging Microscopy Tumor Microenvironment Biopsy Image Interpretation, Computer-Assisted Risk Factors Neoplasm Recurrence, Local Lymphocyte Count Aged Databases, Factual Retrospective Studies Humans Female Machine Learning Automation, Laboratory
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Review of medical image recognition technologies to detect melanomas using neural networks.

BMC bioinformatics Sep 14, 2020
BACKGROUND: Melanoma is one of the most aggressive types of cancer that has become a world-class problem. According to the World Health Organization estimates, 132,000 cases of the disease and 66,000 deaths from malignant melanoma and other forms of ...
Data Accuracy Image Interpretation, Computer-Assisted Deep Learning Early Detection of Cancer Melanoma, Cutaneous Malignant Melanoma Skin Neoplasms Sensitivity and Specificity Humans
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Clinically Relevant Vulnerabilities of Deep Machine Learning Systems for Skin Cancer Diagnosis.

The Journal of investigative dermatology Sep 12, 2020
Diagnosis, Differential Color Skin Image Interpretation, Computer-Assisted Nevus, Pigmented Skin Neoplasms Dermoscopy Deep Learning Humans Melanoma
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A deep learning algorithm with high sensitivity for the detection of basal cell carcinoma in Mohs micrographic surgery frozen sections.

Journal of the American Academy of Dermatology Sep 11, 2020
Humans Skin Neoplasms Mohs Surgery Carcinoma, Basal Cell Deep Learning Frozen Sections
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Diagnosis of congenital pigmented macules in infants with reflectance confocal microscopy and machine learning.

Journal of the American Academy of Dermatology Sep 11, 2020
Dermoscopy Infant Microscopy, Confocal Humans Hutchinson's Melanotic Freckle Machine Learning Diagnosis, Differential Skin Neoplasms Pigmentation Disorders
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Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study.

Journal of medical Internet research Sep 11, 2020
BACKGROUND: Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence (AI) in classifying dermoscopic images of melanoma and nevi, concluding that th...
Dermatologists Surveys and Questionnaires Diagnostic Imaging Internet Humans Skin Neoplasms Dermoscopy Artificial Intelligence Melanoma
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Use of artificial intelligence in skin cancer diagnosis and management.

The Medical journal of Australia Sep 6, 2020
Humans Artificial Intelligence Algorithms Early Detection of Cancer Skin Disease Management Nevus, Pigmented Melanoma Skin Neoplasms Dermoscopy
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Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges.

Journal of infection and public health Aug 2, 2020
Cancer is a fatal illness often caused by genetic disorder aggregation and a variety of pathological changes. Cancerous cells are abnormal areas often growing in any part of human body that are life-threatening. Cancer also known as tumor must be qui...
Precursor Cell Lymphoblastic Leukemia-Lymphoma Skin Neoplasms Sensitivity and Specificity Humans Breast Neoplasms Machine Learning Liver Neoplasms Brain Neoplasms Male Tomography, X-Ray Computed Image Processing, Computer-Assisted Neoplasms Female Lung Neoplasms Surveys and Questionnaires Magnetic Resonance Imaging
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The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning.

Biomolecules Jul 29, 2020
Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the performance of a CNN trained with only clinical image...
Skin Neoplasms Humans Deep Learning Neural Networks, Computer Skin Melanoma
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Guiding biopsies of pigmented lesions with deep learning.

The Australasian journal of dermatology Jul 22, 2020
Humans Melanoma Skin Neoplasms Nevus, Pigmented Deep Learning Image-Guided Biopsy
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