Appropriate maintenance of tracks is vital for the safe operation of railways. Properly managing track facilities is necessary to prevent buckling of rails. Changes in society are creating a shortage of workers, a dec...
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Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new *** learning(ML)models have ...
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Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new *** learning(ML)models have shown satisfactory performance in short-term mortality prediction in patients with heart disease,whereas their utility in long-term predictions is *** study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term *** This study used publicly available data from the Collaboration Center of Health Information Appli-cation at the Ministry of Health and Welfare,Taiwan,*** collected data were from patients admitted to the cardiac care unit for acute myocardial infarction(AMI)between November 2003 and September *** collected and analyzed mortality data up to December *** records were used to gather demo-graphic and clinical data,including age,gender,body mass index,percutaneous coronary intervention status,and comorbidities such as hypertension,dyslipidemia,ST-segment elevation myocardial infarction,and non-ST-segment elevation myocardial *** the data,collected from 139 patients with AMI,from medical and demographic records as well as two recently introduced biomarkers,brachial pre-ejection period(bPEP)and brachial ejection time(bET),we investigated the performance of advanced ensemble tree-based ML algorithms(random forest,AdaBoost,and XGBoost)to predict all-cause mortality within 14 years.A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression(LR)as the baseline *** The developed ML models achieved significantly better performance compared to the baseline LR(C-Statistic,0.80 for random forest,0.79 for AdaBoost,and 0.78 for XGBoost,vs.0.77 for LR)(PRF<0.001,PAdaBoost<0.001,a
In this research, we introduce an innovative saliency detection algorithm, comprising three essential steps. Firstly, leveraging fully convolutional networks with aggregation interaction modules, we generate an initia...
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Early diagnosis of colorectal polyps, before they turn into cancer, is one of the main keys for treatment. In this work, we propose a framework to help radiologists in identifying polyp candidates using virtual colono...
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ISBN:
(数字)9798350313338
ISBN:
(纸本)9798350313345
Early diagnosis of colorectal polyps, before they turn into cancer, is one of the main keys for treatment. In this work, we propose a framework to help radiologists in identifying polyp candidates using virtual colonoscopy. In the proposed approach, first a colon is segmented from a CT scan, then 3D reconstruction, to generate a surface representation of the colon, is performed. From the reconstructed 3D colon, 2D images are generated using the virtual colonoscopy, Fly-In approach. To enhance polyp detection, we fuse these 2D images and the 3D colon representation by generating 3D geometric feature maps, e.g. depth and curvature maps. CNN-based models are trained and validated to detect polyps using the generated feature maps, which are combined in multi-channel images. These images are successfully used to train a CNN-based model that detects polyps with mAP ∼ 97.1%.Colorectal cancer, colon polyp, computerized tomography (CT), Detection, CNN, segmentation.
At present, deep learning technology is widely used in ship target detection in synthetic aperture radar (SAR) images. However, high-resolution remote sensing SAR images cover a larger area and have larger image sizes...
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Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians’ ocular movements during image interpretatio...
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In this work, we propose an automatic colorectal polyps detection approach that consists of two cascade stages. In the first stage, a CNN model is trained to detect polyps in axial CT slices, The CNN model has been fe...
In this work, we propose an automatic colorectal polyps detection approach that consists of two cascade stages. In the first stage, a CNN model is trained to detect polyps in axial CT slices, The CNN model has been fed by the segmented colon wall CT slices instead of the original CT slices. Using the segmented images as an input to the CNN model has drastically improved the detection and localization results, e.g., the mAP is increased by 36%. To reduce the false positives generated by the detector, the second stage classifier is deployed to exploit the different views of the CT scans instead of the axial view only. So, the classifier is trained using the 2D images of axial views, i.e., the candidate polyps generated by the detector, as well as their corresponding 2D images of sagittal and coronal views. The experimental results of this approach were validated by 3 radiologists and the approach successfully identified polyps after the classification stage with an AUC ∼ 98.6%.
Early diagnosis of colorectal polyps, before they turn into cancer, is one of the main keys to treatment. In this work, we propose a framework to help radiologists in reading CT scans and identifying candidate CT slic...
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ISBN:
(数字)9798350349399
ISBN:
(纸本)9798350349405
Early diagnosis of colorectal polyps, before they turn into cancer, is one of the main keys to treatment. In this work, we propose a framework to help radiologists in reading CT scans and identifying candidate CT slices that have polyps. We propose a colorectal polyps detection approach which consists of two cascaded stages. In the first stage, a CNN-based model is trained and validated to detect polyps in axial CT slices. To narrow down the effective receptive field of the detector neurons, the colon regions are segmented and then fed into the network instead of the original CT slice. This drastically improves the detection and localization results, e.g., the mAP is increased by 36%. To reduce the false positives generated by the detector, in the second stage, we propose a multi-view network (MVN) that classifies polyp candidates. The proposed MVN classifier is trained using sagittal and coronal views corresponding to the detected axial views. The approach is tested in 50 CTC-annotated cases, and the experimental results confirm that after the classification stage, polyps can be detected with an AUC $\sim 95.27 \%$.
This study introduces an innovative framework designed specifically for accurate colon segmentation in abdomen CT scans, tackling the distinct challenges inherent to this task. Building upon well-established 2D segmen...
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ISBN:
(数字)9798350349399
ISBN:
(纸本)9798350349405
This study introduces an innovative framework designed specifically for accurate colon segmentation in abdomen CT scans, tackling the distinct challenges inherent to this task. Building upon well-established 2D segmentation models, our architecture adeptly incorporates 3D contextual information via a novel method that generates an attention map for a given slice by considering its neighboring slices in a sequence. Our approach accomplishes effective colon segmentation without requiring complex 3D convolutional neural networks (CNNs) or Long Short-Term Memory (LSTM) networks by combining 2D CNNs. Validated on a dataset of 98 CT scans from 49 patients, the architecture exhibits notable performance, successfully capturing nuanced details crucial for precise colon segmentation. The experiments encompass a thorough examination of model selection and cross-validation, providing valuable insights into the efficacy of our proposed approach. The outcomes underscore the potential for streamlined colon segmentation in medical imaging by judiciously integrating 2D and 3D information, employing solely 2D networks, and mitigating challenges associated with 3D networks. The code for model architecture is available at: https://***/Samir-Farag/***
Deep learning methods have achieved excellent performances on visual tasks of target recognition and classification. The rapid development of autonomous seafaring vessels comes up with the requirement to recognize oth...
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