AIMC Topic: Neural Networks, Computer

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Identifying the vegetation type in Google Earth images using a convolutional neural network: a case study for Japanese bamboo forests.

BMC ecology
BACKGROUND: Classifying and mapping vegetation are crucial tasks in environmental science and natural resource management. However, these tasks are difficult because conventional methods such as field surveys are highly labor-intensive. Identificatio...

Intensity non-uniformity correction in MR imaging using residual cycle generative adversarial network.

Physics in medicine and biology
Correcting or reducing the effects of voxel intensity non-uniformity (INU) within a given tissue type is a crucial issue for quantitative magnetic resonance (MR) image analysis in daily clinical practice. Although having no severe impact on visual di...

Predicting spatial esophageal changes in a multimodal longitudinal imaging study via a convolutional recurrent neural network.

Physics in medicine and biology
Acute esophagitis (AE) occurs among a significant number of patients with locally advanced lung cancer treated with radiotherapy. Early prediction of AE, indicated by esophageal wall expansion, is critical, as it can facilitate the redesign of treatm...

Ultrasound transmission tomography image reconstruction with a fully convolutional neural network.

Physics in medicine and biology
Image reconstruction of ultrasound computed tomography based on the wave equation is able to show much more structural details than simpler ray-based image reconstruction methods. However, to invert the wave-based forward model is computationally dem...

Comparison of 11 automated PET segmentation methods in lymphoma.

Physics in medicine and biology
Segmentation of lymphoma lesions in FDG PET/CT images is critical in both assessing individual lesions and quantifying patient disease burden. Simple thresholding methods remain common despite the large heterogeneity in lymphoma lesion location, size...

Fully automated left atrium segmentation from anatomical cine long-axis MRI sequences using deep convolutional neural network with unscented Kalman filter.

Medical image analysis
This study proposes a fully automated approach for the left atrial segmentation from routine cine long-axis cardiac magnetic resonance image sequences using deep convolutional neural networks and Bayesian filtering. The proposed approach consists of ...

Recognized trophoblast-like cells conversion from human embryonic stem cells by BMP4 based on convolutional neural network.

Reproductive toxicology (Elmsford, N.Y.)
The use of models of stem cell differentiation to trophoblastic cells provides an effective perspective for understanding the early molecular events in the establishment and maintenance of human pregnancy. In combination with the newly developed deep...

DeepPSP: A Global-Local Information-Based Deep Neural Network for the Prediction of Protein Phosphorylation Sites.

Journal of proteome research
Identification of phosphorylation sites is an important step in the function study and drug design of proteins. In recent years, there have been increasing applications of the computational method in the identification of phosphorylation sites becaus...

CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph.

BMC bioinformatics
BACKGROUND: Elucidation of interactive relation between chemicals and genes is of key relevance not only for discovering new drug leads in drug development but also for repositioning existing drugs to novel therapeutic targets. Recently, biological n...