AIMC Topic: Melanoma

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Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers.

Cancer research Dec 21, 2020
Whole-slide histology images contain information that is valuable for clinical and basic science investigations of cancer but extracting quantitative measurements from these images is challenging for researchers who are not image analysis specialists...
Skin Neoplasms Reproducibility of Results Sensitivity and Specificity Breast Neoplasms Algorithms Prognosis Female Melanoma Predictive Value of Tests Melanoma, Cutaneous Malignant Image Interpretation, Computer-Assisted Medical Oncology Datasets as Topic Biomedical Research Neoplasms Humans Image Processing, Computer-Assisted Machine Learning Lymphocytes, Tumor-Infiltrating Software
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A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi.

Journal of dermatological science Dec 2, 2020
BACKGROUND: Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists' experience and fails to achieve adequate...
Humans Retrospective Studies Female Reproducibility of Results Aged Aged, 80 and over Young Adult Infant, Newborn Melanoma Skin Neoplasms Dermoscopy Diagnosis, Differential Deep Learning Skin Adolescent Image Interpretation, Computer-Assisted Child, Preschool Datasets as Topic Child Nevus Adult Male Middle Aged Infant
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Development of a light-weight deep learning model for cloud applications and remote diagnosis of skin cancers.

The Journal of dermatology Nov 19, 2020
Skin cancer is among the 10 most common cancers. Recent research revealed the superiority of artificial intelligence (AI) over dermatologists to diagnose skin cancer from predesignated and cropped images. However, there remain several uncertainties f...
Skin Neoplasms Deep Learning Dermatologists Humans Artificial Intelligence Melanoma
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Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma.

Clinical cancer research : an official journal of the American Association for Cancer Research Nov 18, 2020
PURPOSE: Several biomarkers of response to immune checkpoint inhibitors (ICI) show potential but are not yet scalable to the clinic. We developed a pipeline that integrates deep learning on histology specimens with clinical data to predict ICI respon...
Skin Progression-Free Survival Drug Resistance, Neoplasm Immune Checkpoint Inhibitors Image Processing, Computer-Assisted Follow-Up Studies Neoplasm Staging Middle Aged Risk Assessment Disease Progression ROC Curve Machine Learning Prospective Studies Prognosis Female Aged Adult Male Melanoma Skin Neoplasms Humans
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Use of an Artificial Neural Network to Identify Patient Clusters in a Large Cohort of Patients with Melanoma by Simultaneous Analysis of Costs and Clinical Characteristics.

Acta dermato-venereologica Nov 18, 2020
Cohort Studies Humans Neural Networks, Computer Melanoma
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Deep learning based classification of facial dermatological disorders.

Computers in biology and medicine Nov 13, 2020
Common properties of dermatological diseases are mostly lesions with abnormal pattern and skin color (usually redness). Therefore, dermatology is one of the most appropriate areas in medicine for automated diagnosis from images using pattern recognit...
Humans Melanoma Skin Neoplasms Dermoscopy Deep Learning Skin Diseases
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Artificial intelligence for melanoma diagnosis.

Italian journal of dermatology and venereology Nov 12, 2020
Convolutional neural networks (CNN) have shown unprecedented accuracy in digital image analysis, which can be harnessed for melanoma recognition through automated evaluation of clinical and dermatoscopic images. In experimental studies, modern CNN ar...
Dermoscopy Humans Neural Networks, Computer Artificial Intelligence Prospective Studies Melanoma
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Effective Melanoma Recognition Using Deep Convolutional Neural Network with Covariance Discriminant Loss.

Sensors (Basel, Switzerland) Oct 13, 2020
Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discrim...
Humans Neural Networks, Computer Melanoma Algorithms Skin Neoplasms Dermoscopy Deep Learning
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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...
Microscopy Image Interpretation, Computer-Assisted Lymphocyte Count Retrospective Studies Neoplasm Staging Female Disease Progression Biopsy Melanoma Diagnosis, Computer-Assisted Neoplasm Recurrence, Local Tumor Microenvironment Skin Neoplasms Lymphocytes, Tumor-Infiltrating Progression-Free Survival Reproducibility of Results Male Predictive Value of Tests Humans Middle Aged Risk Factors Machine Learning Risk Assessment Aged Automation, Laboratory Adult Databases, Factual
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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 ...
Humans Image Interpretation, Computer-Assisted Deep Learning Melanoma Early Detection of Cancer Skin Neoplasms Melanoma, Cutaneous Malignant Sensitivity and Specificity Data Accuracy
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