AIMC Topic: Skin Neoplasms

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Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis.

The British journal of dermatology Sep 19, 2018
BACKGROUND: Application of deep-learning technology to skin cancer classification can potentially improve the sensitivity and specificity of skin cancer screening, but the number of training images required for such a system is thought to be extremel...
Skin Neoplasms Dermoscopy Sensitivity and Specificity Humans Skin Dermatologists Image Interpretation, Computer-Assisted Datasets as Topic Deep Learning Smartphone Mobile Applications
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Performance and clinical impact of machine learning based lung nodule detection using vessel suppression in melanoma patients.

Clinical imaging Sep 11, 2018
PURPOSE: To evaluate performance and the clinical impact of a novel machine learning based vessel-suppressing computer-aided detection (CAD) software in chest computed tomography (CT) of patients with malignant melanoma.
Humans Machine Learning Neoplasm Metastasis Multidetector Computed Tomography Skin Neoplasms Adult Aged Aged, 80 and over Retrospective Studies Female Male Lung Neoplasms Melanoma Diagnosis, Computer-Assisted Middle Aged Melanoma, Cutaneous Malignant ROC Curve
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Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features.

IEEE transactions on bio-medical engineering Aug 20, 2018
In this paper, we present a novel framework for dermoscopy image recognition via both a deep learning method and a local descriptor encoding strategy. Specifically, deep representations of a rescaled dermoscopy image are first extracted via a very de...
Dermoscopy Deep Learning Support Vector Machine Image Interpretation, Computer-Assisted Databases, Factual Melanoma Skin Neoplasms Humans Algorithms
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Skin lesion classification with ensembles of deep convolutional neural networks.

Journal of biomedical informatics Aug 10, 2018
Skin cancer is a major public health problem with over 123,000 newly diagnosed cases worldwide in every year. Melanoma is the deadliest form of skin cancer, responsible for over 9000 deaths in the United States each year. Thus, reliable automatic mel...
Skin Neoplasms Dermoscopy Diagnosis, Computer-Assisted Algorithms Databases, Factual Melanoma Keratosis, Seborrheic Melanocytes ROC Curve Image Processing, Computer-Assisted Nevus Neural Networks, Computer Machine Learning Humans
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Dense Deconvolutional Network for Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics Jul 25, 2018
Automatic delineation of skin lesion contours from dermoscopy images is a basic step in the process of diagnosis and treatment of skin lesions. However, it is a challenging task due to the high variation of appearances and sizes of skin lesions. In o...
Image Interpretation, Computer-Assisted Humans Skin Neoplasms Neural Networks, Computer Dermoscopy Algorithms
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Machine learning model in melanoma.

The Lancet. Oncology Jun 7, 2018
Sensitivity and Specificity Male Diagnosis, Computer-Assisted Germany Clinical Competence Dermatologists Female Melanoma Skin Neoplasms Humans Machine Learning Retrospective Studies
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Automated Dermatological Diagnosis: Hype or Reality?

The Journal of investigative dermatology Jun 1, 2018
Humans Algorithms Skin Neoplasms Deep Learning
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Interpretation of the Outputs of a Deep Learning Model Trained with a Skin Cancer Dataset.

The Journal of investigative dermatology Jun 1, 2018
Deep Learning Skin Humans Algorithms Skin Neoplasms
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Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features.

IEEE journal of biomedical and health informatics May 1, 2018
The presence of certain clinical dermoscopic features within a skin lesion may indicate melanoma, and automatically detecting these features may lead to more quantitative and reproducible diagnoses. We reformulate the task of classifying clinical der...
Humans Neural Networks, Computer Dermoscopy ROC Curve Skin Databases, Factual Image Interpretation, Computer-Assisted Melanoma Skin Neoplasms
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Control of Gene Regulatory Networks Using Bayesian Inverse Reinforcement Learning.

IEEE/ACM transactions on computational biology and bioinformatics Apr 26, 2018
Control of gene regulatory networks (GRNs) to shift gene expression from undesirable states to desirable ones has received much attention in recent years. Most of the existing methods assume that the cost of intervention at each state and time point,...
Wnt-5a Protein Gene Expression Profiling Models, Biological Gene Regulatory Networks Algorithms Humans Gene Expression Regulation, Neoplastic Computational Biology Tumor Suppressor Protein p53 Melanoma Models, Genetic Machine Learning Proto-Oncogene Proteins c-mdm2 Skin Neoplasms Bayes Theorem Software
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