scholarly journals Estimation of the radiation dose in pregnancy: an automated patient-specific model using convolutional neural networks

2019 ◽  
Vol 29 (12) ◽  
pp. 6805-6815 ◽  
Author(s):  
Tianwu Xie ◽  
Habib Zaidi
Keyword(s):  
Neural Networks ◽  
Radiation Dose ◽  
Convolutional Neural Networks ◽  
Specific Model ◽  
Patient Specific ◽  
In Pregnancy

One-Dimensional Convolutional Neural Networks Combined with Channel Selection Strategy for Seizure Prediction Using Long-Term Intracranial EEG

2021 ◽  
Author(s):  
Xiaoshuang Wang ◽  
Guanghui Zhang ◽  
Ying Wang ◽  
Lin Yang ◽  
Zhanhua Liang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Single Channel ◽  
Channel Selection ◽  
Specific Model ◽  
Patient Specific ◽  
Selection Strategy ◽  
Seizure Prediction ◽  
Seizure Onset ◽  
One Dimensional

Seizure prediction using intracranial electroencephalogram (iEEG) has attracted an increasing attention during recent years. iEEG signals are commonly recorded in the form of multiple channels. Many previous studies generally used the iEEG signals of all channels to predict seizures, ignoring the consideration of channel selection. In this study, a method of one-dimensional convolutional neural networks (1D-CNN) combined with channel selection strategy was proposed for seizure prediction. First, we used 30-s sliding windows to segment the raw iEEG signals. Then, the 30-s iEEG segments, which were in three channel forms (single channel, channels only from seizure onset or free zone and all channels from seizure onset and free zones), were used as the inputs of 1D-CNN for classification, and the patient-specific model was trained. Finally, the channel form with the best classification was selected for each patient. The proposed method was evaluated on the Freiburg Hospital iEEG dataset. In the situation of seizure occurrence period (SOP) of 30[Formula: see text]min and seizure prediction horizon (SPH) of 5[Formula: see text]min, 98.60[Formula: see text] accuracy, 98.85[Formula: see text] sensitivity and 0.01/h false prediction rate (FPR) were achieved. In the situation of SOP of 60[Formula: see text]min and SPH of 5[Formula: see text]min, 98.32[Formula: see text] accuracy, 98.48[Formula: see text] sensitivity and 0.01/h FPR were attained. Compared with the many existing methods using the same iEEG dataset, our method showed a better performance.

scholarly journals Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer

2020 ◽  
Author(s):  
Hryhorii Chereda ◽  
Annalen Bleckmann ◽  
Kerstin Menck ◽  
Júlia Perera-Bel ◽  
Philip Stegmaier ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Gene Expression Data ◽  
Molecular Network ◽  
Patient Specific ◽  
Expression Data ◽  
Learning Methods

AbstractMotivationContemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models. Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. It is widely used to interpret Convolutional Neural Networks (CNNs) applied on image data. Recently, CNNs started to extend towards non-euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be applied to structured gene expression data, for example, to predict metastatic events in breast cancer. Therefore, there is a need for explanations showing which part of a molecular network is relevant for predicting an event, e.g. distant metastasis in cancer, for each individual patient.ResultsWe extended the procedure of LRP to make it available for Graph-CNN and tested its applicability on a large breast cancer dataset. We present Graph Layer-wise Relevance Propagation (GLRP) as a new method to explain the decisions made by Graph-CNNs. We demonstrate a sanity check of the developed GLRP on a hand-written digits dataset, and then applied the method on gene expression data. We show that GLRP provides patient-specific molecular subnetworks that largely agree with clinical knowledge and identify common as well as novel, and potentially druggable, drivers of tumor progression. As a result this method could be potentially highly useful on interpreting classification results on the individual patient level, as for example in precision medicine approaches or a molecular tumor board.Availabilityhttps://gitlab.gwdg.de/UKEBpublic/graph-lrphttps://frankkramer-lab.github.io/MetaRelSubNetVis/[email protected]

scholarly journals Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification

2020 ◽  
Vol 7 (06) ◽  
Author(s):  
Mariëlle J. A. Jansen ◽  
Hugo J. Kuijf ◽  
Ashis K. Dhara ◽  
Nick A. Weaver ◽  
Geert Jan Biessels ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fine Tuning ◽  
Patient Specific

Deep learning for patient‐specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks

2018 ◽  
Vol 46 (2) ◽  
pp. 456-464 ◽  
Author(s):  
Matthew J. Nyflot ◽  
Phawis Thammasorn ◽  
Landon S. Wootton ◽  
Eric C. Ford ◽  
W. Art Chaovalitwongse
Keyword(s):  
Neural Networks ◽  
Quality Assurance ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Patient Specific

scholarly journals Classification of moving coronary calcified plaques based on motion artifacts using convolutional neural networks: a robotic simulating study on influential factors

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Magdalena Dobrolińska ◽  
Niels van der Werf ◽  
Marcel Greuter ◽  
Beibei Jiang ◽  
Riemer Slart ◽  
...  
Keyword(s):  
Neural Networks ◽  
Radiation Dose ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Classification Performance ◽  
Influential Factors ◽  
Motion Artifacts ◽  
Reconstruction Method ◽  
Reconstruction Algorithms ◽  
Ct System

Abstract Background Motion artifacts affect the images of coronary calcified plaques. This study utilized convolutional neural networks (CNNs) to classify the motion-contaminated images of moving coronary calcified plaques and to determine the influential factors for the classification performance. Methods Two artificial coronary arteries containing four artificial plaques of different densities were placed on a robotic arm in an anthropomorphic thorax phantom. Each artery moved linearly at velocities ranging from 0 to 60 mm/s. CT examinations were performed with four state-of-the-art CT systems. All images were reconstructed with filtered back projection and at least three levels of iterative reconstruction. Each examination was performed at 100%, 80% and 40% radiation dose. Three deep CNN architectures were used for training the classification models. A five-fold cross-validation procedure was applied to validate the models. Results The accuracy of the CNN classification was 90.2 ± 3.1%, 90.6 ± 3.5%, and 90.1 ± 3.2% for the artificial plaques using Inception v3, ResNet101 and DenseNet201 CNN architectures, respectively. In the multivariate analysis, higher density and increasing velocity were significantly associated with higher classification accuracy (all P < 0.001). The classification accuracy in all three CNN architectures was not affected by CT system, radiation dose or image reconstruction method (all P > 0.05). Conclusions The CNN achieved a high accuracy of 90% when classifying the motion-contaminated images into the actual category, regardless of different vendors, velocities, radiation doses, and reconstruction algorithms, which indicates the potential value of using a CNN to correct calcium scores.

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