AIMC Topic: Melanoma

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Detection of malignant melanoma in H&E-stained images using deep learning techniques.

Tissue & cell Sep 29, 2021
Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei an...
Humans Neural Networks, Computer Algorithms Image Processing, Computer-Assisted Staining and Labeling Melanoma, Cutaneous Malignant Cell Nucleus Melanoma Skin Neoplasms Deep Learning Eosine Yellowish-(YS) Hematoxylin
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Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting.

PloS one Sep 22, 2021
Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expectancy, along with new diagnostic tools and treatments for skin cancer, has resulted in unprecedented changes in patient care and has generated a great...
Area Under Curve Physicians, Primary Care Melanoma Adult Skin Neoplasms Smartphone Humans Diagnosis, Computer-Assisted Sensitivity and Specificity Dermoscopy Early Detection of Cancer Artificial Intelligence Male Surveys and Questionnaires Female
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Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.

European journal of cancer (Oxford, England : 1990) Sep 8, 2021
BACKGROUND: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinic...
Automation Reproducibility of Results Dermatologists Neural Networks, Computer Diagnosis, Computer-Assisted Microscopy Melanoma Deep Learning Image Interpretation, Computer-Assisted Skin Neoplasms Biopsy Humans Dermoscopy Clinical Competence Pathologists Predictive Value of Tests
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AI outperformed every dermatologist in dermoscopic melanoma diagnosis, using an optimized deep-CNN architecture with custom mini-batch logic and loss function.

Scientific reports Sep 1, 2021
Melanoma, one of the most dangerous types of skin cancer, results in a very high mortality rate. Early detection and resection are two key points for a successful cure. Recent researches have used artificial intelligence to classify melanoma and nevu...
Humans Neural Networks, Computer Artificial Intelligence Dermoscopy Deep Learning ROC Curve Dermatologists Algorithms Melanoma Skin Neoplasms
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Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering.

Microscopy research and technique Aug 27, 2021
Melanoma skin cancer is the most life-threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effective...
Cluster Analysis Skin Neoplasms Algorithms Dermoscopy Melanoma Deep Learning Humans
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Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

eLife Aug 5, 2021
For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrai...
Rhinovirus Adolescent Unsupervised Machine Learning CD4-Positive T-Lymphocytes Melanoma SARS-CoV-2 Young Adult Neoplasms Picornaviridae Infections Humans COVID-19 Adult Algorithms Leukemia, Myeloid, Acute
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Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours.

European journal of cancer (Oxford, England : 1990) Jul 20, 2021
AIM: Sentinel lymph node status is a central prognostic factor for melanomas. However, the surgical excision involves some risks for affected patients. In this study, we therefore aimed to develop a digital biomarker that can predict lymph node metas...
Lymphatic Metastasis Sentinel Lymph Node Aged Adult Middle Aged Deep Learning Humans Melanoma
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An Evolutionary Approach for the Enhancement of Dermatological Images and Their Classification Using Deep Learning Models.

Journal of healthcare engineering Jul 15, 2021
Dermatological problems are the most widely spread skin diseases amongst human beings. They can be infectious, chronic, and sometimes may also lead to serious health problems such as skin cancer. Generally, rural area clinics lack trained dermatologi...
Neural Networks, Computer Melanoma Skin Neoplasms Humans Carcinoma, Basal Cell Deep Learning
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Reproducible Naevus Counts Using 3D Total Body Photography and Convolutional Neural Networks.

Dermatology (Basel, Switzerland) Jul 8, 2021
BACKGROUND: The number of naevi on a person is the strongest risk factor for melanoma; however, naevus counting is highly variable due to lack of consistent methodology and lack of inter-rater agreement. Machine learning has been shown to be a valuab...
Humans Melanoma Skin Neoplasms Early Detection of Cancer Neural Networks, Computer Photography Female Whole Body Imaging Sensitivity and Specificity Male Nevus Reproducibility of Results Middle Aged Aged Imaging, Three-Dimensional Adult
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Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma.

Cell systems Jun 1, 2021
Deep learning has emerged as the technique of choice for identifying hidden patterns in cell imaging data but is often criticized as "black box." Here, we employ a generative neural network in combination with supervised machine learning to classify ...
Mice Artificial Intelligence Melanoma Humans Animals Deep Learning Neural Networks, Computer
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