scholarly journals External Validation of Deep Learning-Based Artifact Correction on a Synthetic FLAIR image using Convolutional Deep Neural Network: Can it be a Promising Solution for Image Quality Improvement?

2020 ◽  
Author(s):  
Hye Jin Baek
Keyword(s):  
Neural Network ◽  
Quality Improvement ◽  
Deep Learning ◽  
Image Quality ◽  
Deep Neural Network ◽  
External Validation ◽  
Flair Image ◽  
Artifact Correction ◽  
Image Quality Improvement ◽  
Promising Solution

Image quality improvement in cone-beam CT using deep learning

2019 ◽  
Author(s):  
Yang Lei ◽  
Tonghe Wang ◽  
Joseph Harms ◽  
Ghazal Shafai-Erfani ◽  
Xue Dong ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Deep Learning ◽  
Image Quality ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Image Quality Improvement

scholarly journals Evaluating Deep Learning models for predicting ALK-5 inhibition

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0246126
Author(s):  
Gabriel Z. Espinoza ◽  
Rafaela M. Angelo ◽  
Patricia R. Oliveira ◽  
Kathia M. Honorio
Keyword(s):  
Neural Network ◽  
Biological Activity ◽  
Deep Learning ◽  
Deep Neural Network ◽  
External Validation ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Learning Models ◽  
Alk 5

Computational methods have been widely used in drug design. The recent developments in machine learning techniques and the ever-growing chemical and biological databases are fertile ground for discoveries in this area. In this study, we evaluated the performance of Deep Learning models in comparison to Random Forest, and Support Vector Regression for predicting the biological activity (pIC50) of ALK-5 inhibitors as candidates to treat cancer. The generalization power of the models was assessed by internal and external validation procedures. A deep neural network model obtained the best performance in this comparative study, achieving a coefficient of determination of 0.658 on the external validation set with mean square error and mean absolute error of 0.373 and 0.450, respectively. Additionally, the relevance of the chemical descriptors for the prediction of biological activity was estimated using Permutation Importance. We can conclude that the forecast model obtained by the deep neural network is suitable for the problem and can be employed to predict the biological activity of new ALK-5 inhibitors.

scholarly journals An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles

2021 ◽  
Author(s):  
Frank Niemeyer ◽  
Annika Zanker ◽  
René Jonas ◽  
Youping Tao ◽  
Fabio Galbusera ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Scale ◽  
Deep Neural Network ◽  
External Validation ◽  
Erector Spinae ◽  
Published Data ◽  
Spinal Disorders ◽  
Paravertebral Muscles ◽  
Mri Scans

Purpose. Imaging studies about the relevance of muscles in spinal disorders, and sarcopenia in general, require the segmentation of the muscles in the images which is very labour-intensive if performed manually and poses a practical limit to the number of investigated subjects. This study aimed at developing a deep learning-based tool able to fully automatically perform an accurate segmentation of the lumbar muscles in axial MRI scans, and at validating the new tool on an external dataset. Methods. A set of 60 axial MRI images of the lumbar spine was retrospectively collected from a clinical database. Psoas major, quadratus lumborum, erector spinae, and multifidus were manually segmented in all available slices. The dataset was used to train and validate a deep neural network able to segment muscles automatically. Subsequently, the network was externally validated on images purposely acquired from 22 healthy volunteers. Results. The Jaccard index for the individual muscles calculated for the 22 subjects of the external validation set ranged between 0.862 and 0.935, demonstrating a generally excellent performance of the network. Cross-sectional area and fat fraction of the muscles were in agreement with published data. Conclusions. The externally validated deep neural network was able to perform the segmentation of the paravertebral muscles in axial MRI scans in an accurate and fully automated manner, and is therefore a suitable tool to perform large-scale studies in the field of spinal disorders and sarcopenia, overcoming the limitations of non-automated methods.

scholarly journals Systematic infrared image quality improvement using deep learning based techniques

2016 ◽  
Author(s):  
Huaizhong Zhang ◽  
Pablo Casaseca-de-la-Higuera ◽  
Chunbo Luo ◽  
Qi Wang ◽  
Matthew Kitchin ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Deep Learning ◽  
Image Quality ◽  
Infrared Image ◽  
Image Quality Improvement
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