AIMC Topic: Melanoma, Cutaneous Malignant

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Computer-aided detection and segmentation of malignant melanoma lesions on whole-body F-FDG PET/CT using an interpretable deep learning approach.

Computer methods and programs in biomedicine May 22, 2022
BACKGROUND AND OBJECTIVE: In oncology, 18-fluorodeoxyglucose (F-FDG) positron emission tomography (PET) / computed tomography (CT) is widely used to identify and analyse metabolically-active tumours. The combination of the high sensitivity and specif...
Humans Computers Radiopharmaceuticals Melanoma, Cutaneous Malignant Positron-Emission Tomography Fluorodeoxyglucose F18 Melanoma Skin Neoplasms Deep Learning Positron Emission Tomography Computed Tomography
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Deep Learning Algorithms for Predicting Breslow Thickness from Dermoscopic Images of Acral Lentiginous Melanomas.

The Journal of investigative dermatology Jan 12, 2022
Deep Learning Melanoma, Cutaneous Malignant Retrospective Studies Melanoma Humans Skin Neoplasms
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Prognostic Value of Vitamin D Serum Levels in Cutaneous Melanoma.

Actas dermo-sifiliograficas Nov 16, 2021
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...
Melanoma, Cutaneous Malignant Melanoma Skin Neoplasms Humans Vitamin D Prognosis Vitamins Retrospective Studies
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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...
Image Processing, Computer-Assisted Algorithms Cell Nucleus Staining and Labeling Humans Melanoma, Cutaneous Malignant Melanoma Skin Neoplasms Eosine Yellowish-(YS) Neural Networks, Computer Deep Learning Hematoxylin
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Ultra-high-frequency ultrasound and machine learning approaches for the differential diagnosis of melanocytic lesions.

Experimental dermatology Mar 31, 2021
Malignant melanoma (MM) is one of the most dangerous skin cancers. The aim of this study was to present a potential new method for the differential diagnosis of MM from melanocytic naevi (MN). We examined 20 MM and 19 MN with a new ultra-high-frequen...
Machine Learning Female Melanoma Skin Neoplasms Humans Sensitivity and Specificity Male Middle Aged Ultrasonography Aged Aged, 80 and over Adult Melanoma, Cutaneous Malignant
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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...
Medical Oncology Datasets as Topic Biomedical Research Neoplasms Image Processing, Computer-Assisted Lymphocytes, Tumor-Infiltrating Predictive Value of Tests Melanoma, Cutaneous Malignant Image Interpretation, Computer-Assisted Skin Neoplasms Reproducibility of Results Sensitivity and Specificity Humans Breast Neoplasms Machine Learning Prognosis Software Female Algorithms Melanoma
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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 ...
Melanoma, Cutaneous Malignant Data Accuracy Image Interpretation, Computer-Assisted Skin Neoplasms Sensitivity and Specificity Humans Deep Learning Melanoma Early Detection of Cancer
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New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation.

BioMed research international Apr 13, 2020
In this paper, an algorithm is introduced to solve the global optimization problem for melanoma skin cancer segmentation. The algorithm is based on the smoothing of an auxiliary function that is constructed using a known local minimizer and smoothed ...
Humans Neural Networks, Computer Algorithms Melanoma Skin Neoplasms Diagnosis, Computer-Assisted Image Processing, Computer-Assisted Melanoma, Cutaneous Malignant
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Histopathology-guided mass spectrometry differentiates benign nevi from malignant melanoma.

Journal of cutaneous pathology Nov 27, 2019
PURPOSE: Distinguishing benign nevi from malignant melanoma using current histopathological criteria may be very challenging and is one the most difficult areas in dermatopathology. The goal of this study was to identify proteomic differences, which ...
Machine Learning Female Melanoma Skin Neoplasms Mass Spectrometry Humans Adult Diagnosis, Differential Male Melanoma, Cutaneous Malignant Middle Aged Aged Nevus Child, Preschool Adolescent Aged, 80 and over Child Young Adult Proteomics
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Gabor wavelet-based deep learning for skin lesion classification.

Computers in biology and medicine Sep 4, 2019
Skin cancer cases are increasing and becoming one of the main problems worldwide. Skin cancer is known as a malignant type of skin lesion, and early detection and treatment are necessary. Malignant melanoma and seborrheic keratosis are known as commo...
Skin Humans Melanoma, Cutaneous Malignant Melanoma Skin Neoplasms Dermoscopy Deep Learning Image Processing, Computer-Assisted
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