AIMC Topic: Skin Neoplasms

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Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks.

IEEE journal of biomedical and health informatics Dec 25, 2017
Automatic skin lesion segmentation on dermoscopic images is an essential step in computer-aided diagnosis of melanoma. However, this task is challenging due to significant variations of lesion appearances across different patients. This challenge is ...
Humans Algorithms Dermoscopy Deep Learning Skin Image Interpretation, Computer-Assisted Databases, Factual Melanoma Skin Neoplasms
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From word models to executable models of signaling networks using automated assembly.

Molecular systems biology Nov 24, 2017
Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to b...
Humans Neural Networks, Computer Computer Simulation Language Vemurafenib Tumor Suppressor Protein p53 Proto-Oncogene Proteins p21(ras) Proto-Oncogene Proteins B-raf Signal Transduction Cell Line, Tumor Natural Language Processing Gene Expression Regulation, Neoplastic Enzyme Inhibitors DNA Damage Sulfonamides Melanoma Models, Genetic Indoles Skin Neoplasms Drug Resistance, Neoplasm Antineoplastic Agents
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Melanoma segmentation based on deep learning.

Computer assisted surgery (Abingdon, England) Oct 18, 2017
Malignant melanoma is one of the most deadly forms of skin cancer, which is one of the world's fastest-growing cancers. Early diagnosis and treatment is critical. In this study, a neural network structure is utilized to construct a broad and accurate...
Early Detection of Cancer Melanoma, Cutaneous Malignant Neural Networks, Computer Algorithms Melanoma Deep Learning Image Processing, Computer-Assisted Skin Neoplasms Reproducibility of Results Humans
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Machine learning and melanoma: The future of screening.

Journal of the American Academy of Dermatology Oct 6, 2017
Machine Learning Melanoma Skin Neoplasms Humans Dermoscopy Sensitivity and Specificity Early Detection of Cancer
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Rapid and accurate intraoperative pathological diagnosis by artificial intelligence with deep learning technology.

Medical hypotheses Sep 1, 2017
Frozen section is widely used for intraoperative pathological diagnosis (IOPD), which is essential for intraoperative decision making. However, frozen section suffers from some drawbacks, such as time consuming and high misdiagnosis rate. Recently, a...
Decision Making, Computer-Assisted Frozen Sections Diagnosis, Computer-Assisted Models, Theoretical Intraoperative Period Humans Skin Neoplasms Artificial Intelligence Diagnostic Errors Machine Learning
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Employing decomposable partially observable Markov decision processes to control gene regulatory networks.

Artificial intelligence in medicine Jul 18, 2017
OBJECTIVE: Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs).
Artificial Intelligence Databases, Genetic Melanoma Markov Chains Humans Skin Neoplasms Gene Regulatory Networks Gene Expression Profiling Computational Biology Transcriptome Gene Expression Regulation, Neoplastic
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Skin cancer diagnosed using artificial intelligence on clinical images.

Oral diseases Apr 17, 2017
Humans Artificial Intelligence Skin Neoplasms
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Dermatologist-level classification of skin cancer with deep neural networks.

Nature Jan 25, 2017
Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of ski...
Keratosis, Seborrheic Photography Cell Phone Nevus Keratinocytes Datasets as Topic Automation Melanoma Humans Skin Neoplasms Reproducibility of Results Neural Networks, Computer Dermatologists
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Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model.

IEEE transactions on medical imaging Dec 1, 2016
We develop a novel method for classifying melanocytic tumors as benign or malignant by the analysis of digital dermoscopy images. The algorithm follows three steps: first, lesions are extracted using a self-generating neural network (SGNN); second, f...
Melanoma Skin Neoplasms Dermoscopy Humans Neural Networks, Computer Algorithms Image Interpretation, Computer-Assisted
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Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines.

BioMed research international Jun 13, 2016
Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less exte...
Sensitivity and Specificity Diagnosis, Computer-Assisted Skin Neoplasms Early Diagnosis Humans Dermoscopy Image Enhancement Support Vector Machine Software Design Algorithms Reproducibility of Results Melanoma Nevus
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