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Melanoma

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Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features.

IEEE journal of biomedical and health informatics
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...

Control of Gene Regulatory Networks Using Bayesian Inverse Reinforcement Learning.

IEEE/ACM transactions on computational biology and bioinformatics
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,...

Acral melanoma detection using a convolutional neural network for dermoscopy images.

PloS one
BACKGROUND/PURPOSE: Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the h...

Machine learning-based diagnosis of melanoma using macro images.

International journal for numerical methods in biomedical engineering
Cancer bears a poisoning threat to human society. Melanoma, the skin cancer, originates from skin layers and penetrates deep into subcutaneous layers. There exists an extensive research in melanoma diagnosis using dermatoscopic images captured throug...

DermaKNet: Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis.

IEEE journal of biomedical and health informatics
Traditional approaches to automatic diagnosis of skin lesions consisted of classifiers working on sets of hand-crafted features, some of which modeled lesion aspects of special importance for dermatologists. Recently, the broad adoption of convolutio...

Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks.

IEEE journal of biomedical and health informatics
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 ...

Effect of dacarbazine on CD44 in live melanoma cells as measured by atomic force microscopy-based nanoscopy.

International journal of nanomedicine
CD44 ligand-receptor interactions are known to be involved in regulating cell migration and tumor cell metastasis. High expression levels of CD44 correlate with a poor prognosis of melanoma patients. In order to understand not only the mechanistic ba...

Salinomycin-loaded lipid-polymer nanoparticles with anti-CD20 aptamers selectively suppress human CD20+ melanoma stem cells.

Acta pharmacologica Sinica
Melanoma is the deadliest type of skin cancer. CD20+ melanoma stem cells (CSCs) are pivotal for metastasis and initiation of melanoma. Therefore, selective elimination of CD20+ melanoma CSCs represents an effective treatment to eradicate melanoma. Sa...

Melanoma segmentation based on deep learning.

Computer assisted surgery (Abingdon, England)
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...