AIMC Topic: Skin Neoplasms

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Perceptions of the use of artificial intelligence in the diagnosis of skin cancer: an outpatient survey.

Clinical and experimental dermatology
BACKGROUND: Convolutional neural networks (artificial intelligence, AI) are rapidly appearing within the field of dermatology, with diagnostic accuracy matching that of dermatologists. As technologies become available for use by both the health profe...

Machine Learning and Its Application in Skin Cancer.

International journal of environmental research and public health
Artificial intelligence (AI) has wide applications in healthcare, including dermatology. Machine learning (ML) is a subfield of AI involving statistical models and algorithms that can progressively learn from data to predict the characteristics of ne...

Deep learning data augmentation for Raman spectroscopy cancer tissue classification.

Scientific reports
Recently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in revealing molecular biochemical changes between cancerous vs. normal tissues and cells. In order to design...

Deep Learning-Based Transfer Learning for Classification of Skin Cancer.

Sensors (Basel, Switzerland)
One of the major health concerns for human society is skin cancer. When the pigments producing skin color turn carcinogenic, this disease gets contracted. A skin cancer diagnosis is a challenging process for dermatologists as many skin cancer pigment...

Robotic Extended Ultrasound-Guided Distal Pancreatectomy for Pancreatic Metastases from Uveal Melanoma.

Annals of surgical oncology
BACKGROUND: Isolated pancreatic metastasis from melanoma is extremely uncommon and accounts for approximately only 2% of visceral disseminations of melanoma. Interestingly, pancreatic localizations disproportionately derive from primary ocular melano...

Multi-features extraction based on deep learning for skin lesion classification.

Tissue & cell
For various forms of skin lesion, many different feature extraction methods have been investigated so far. Indeed, feature extraction is a crucial step in machine learning processes. In general, we can distinct handcrafted and deep learning features....

Artificial Intelligence for Skin Cancer Detection: Scoping Review.

Journal of medical Internet research
BACKGROUND: Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, artificial intelligence (AI) tool...

A disease network-based deep learning approach for characterizing melanoma.

International journal of cancer
Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic features may have an impact on the prognosis of the disease. The access to genomics data via public repositories such as The Cancer Genome Atlas (TCGA) all...

Prognostic Value of Vitamin D Serum Levels in Cutaneous Melanoma.

Actas dermo-sifiliograficas
INTRODUCTION: Vitamin D plays a fundamental role in many metabolic pathways, including those involved in cell proliferation and the immune response. Serum levels of this vitamin have been linked to melanoma risk and prognosis. This study aimed to ass...

Characteristics of publicly available skin cancer image datasets: a systematic review.

The Lancet. Digital health
Publicly available skin image datasets are increasingly used to develop machine learning algorithms for skin cancer diagnosis. However, the total number of datasets and their respective content is currently unclear. This systematic review aimed to id...