scholarly journals UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rui Miao ◽  
Xin Dong ◽  
Sheng-Li Xie ◽  
Yong Liang ◽  
Sio-Long Lo
Keyword(s):  
Recognition Task ◽  
Training Model ◽  
Learning Model ◽  
Model Parameters ◽  
X Ray ◽  
Learning Framework ◽  
Fast Screening ◽  
Rapid Spread ◽  
Meta Learning ◽  
Multi Class Classification

Abstract Background With the rapid spread of COVID-19 worldwide, quick screening for possible COVID-19 patients has become the focus of international researchers. Recently, many deep learning-based Computed Tomography (CT) image/X-ray image fast screening models for potential COVID-19 patients have been proposed. However, the existing models still have two main problems. First, most of the existing supervised models are based on pre-trained model parameters. The pre-training model needs to be constructed on a dataset with features similar to those in COVID-19 X-ray images, which limits the construction and use of the model. Second, the number of categories based on the X-ray dataset of COVID-19 and other pneumonia patients is usually imbalanced. In addition, the quality is difficult to distinguish, leading to non-ideal results with the existing model in the multi-class classification COVID-19 recognition task. Moreover, no researchers have proposed a COVID-19 X-ray image learning model based on unsupervised meta-learning. Methods This paper first constructed an unsupervised meta-learning model for fast screening of COVID-19 patients (UMLF-COVID). This model does not require a pre-trained model, which solves the limitation problem of model construction, and the proposed unsupervised meta-learning framework solves the problem of sample imbalance and sample quality. Results The UMLF-COVID model is tested on two real datasets, each of which builds a three-category and four-category model. And the experimental results show that the accuracy of the UMLF-COVID model is 3–10% higher than that of the existing models. Conclusion In summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.

scholarly journals Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning

2020 ◽  
Vol 34 (07) ◽  
pp. 11205-11212 ◽  
Author(s):  
Ilchae Jung ◽  
Kihyun You ◽  
Hyeonwoo Noh ◽  
Minsu Cho ◽  
Bohyung Han
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Ground Truth ◽  
Learning Task ◽  
Model Parameters ◽  
Model Adaptation ◽  
Learning Framework ◽  
Learning Rates ◽  
Meta Learning ◽  
The Cost

We propose a novel meta-learning framework for real-time object tracking with efficient model adaptation and channel pruning. Given an object tracker, our framework learns to fine-tune its model parameters in only a few gradient-descent iterations during tracking while pruning its network channels using the target ground-truth at the first frame. Such a learning problem is formulated as a meta-learning task, where a meta-tracker is trained by updating its meta-parameters for initial weights, learning rates, and pruning masks through carefully designed tracking simulations. The integrated meta-tracker greatly improves tracking performance by accelerating the convergence of online learning and reducing the cost of feature computation. Experimental evaluation on the standard datasets demonstrates its outstanding accuracy and speed compared to the state-of-the-art methods.

scholarly journals Personalized Federated Learning with Semisupervised Distillation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xianxian Li ◽  
Yanxia Gong ◽  
Yuan Liang ◽  
Li-e Wang
Keyword(s):  
Model Performance ◽  
Personal Data ◽  
Training Model ◽  
Heterogeneous Data ◽  
Global Model ◽  
Model Parameters ◽  
Quality Model ◽  
Learning Framework ◽  
Class Distribution ◽  
Adaptive Aggregation

Heterogeneous data and models pose critical challenges for federated learning. However, the traditional federated learning framework, which trains the global model by transferring model parameters, has major limitations; it requires that all participants have the same training model architectures, and the trained global model does not guarantee accurate projections for participants’ personal data. To solve this problem, we propose a new federal framework named personalized federated learning with semisupervised distillation (pFedSD), which ensures the privacy of the participants’ model architectures and improves the communication efficiency by transmitting the model’s predicted class distribution rather than model parameters. First, the server adopts the adaptive aggregation method to reduce the weight of low-quality model predictions for the model’s predicted class distributions uploaded by all clients, which helps to improve the quality of the aggregation of the prediction class distribution. Then, the server sends it back to the clients for local training to obtain the personalized model. We finally conducted experiments on different datasets (MNIST, FMNIST, and CIFAR10), and the results show that the model performance of pFedSD exceeds the latest federated distillation algorithms.

scholarly journals Automatic Tooth Segmentation and Classification in Dental Panoramic X-ray Images

2020 ◽  
Author(s):  
Shuxu Zhao ◽  
QING LUO ◽  
Changrong Liu
Keyword(s):  
Recognition Task ◽  
Recall Rate ◽  
X Ray ◽  
Learning Framework ◽  
Tooth Position ◽  
Data Engineering ◽  
Segmentation Task ◽  
Instance Segmentation ◽  
Tooth Segmentation

Abstract Background: The information of tooth shape, type and tooth position plays an important role in the understanding of pathological features in dental X-ray films. It is of great significance to realize the accurate tooth segmentation and tooth classification of dental panoramic X-ray images for the construction of an intelligent dental diagnosis system.At present, the segmentation results of teeth are relatively rough, and most methods realize tooth recognition and segmentation as independent tasks, ignoring the parameter sharing between the two tasks. Therefore, an instance segmentation method which can realize tooth recognition and tooth segmentation at the same time is proposed. Methods: In model designing, the Mask R-CNN, an instance segmentation model , is adopted, which includes classification branches and segmentation branches. The classification branch can be used to complete the tooth recognition task and the segmentation branch to complete the tooth segmentation task. On this basis, the U-Net architecture is integrated to modify the segmentation branch to improve the segmentation effect. In data engineering, two classification schemes are designed, one according to the function of teeth, the other according to the position of teeth. Results: Based on the data of 400 panoramic X-ray films of teeth, we combined migration learning to conduct experiments on the TensorFlow deep learning framework. The experimental results show that compared with other methods, the classification and segmentation of teeth can be realized simultaneously in this paper, with an accuracy of more than 90%. Compared with the original model, the improved Mask R-CNN proposed in this paper improves the segmentation recall rate by 10%. In the proposed classification scheme, the accuracy of classification based on tooth function is 3% higher than that based on tooth position.Conclusions: The model proposed in this paper combines the two tasks of classification and segmentation, avoids the repetitive training of the model, and improves the segmentation precision with the improved segmentation branch. Compared with the recall rate traditional methods of tooth function classification, the proposed method based on tooth function has better classification effect.

Use It and Still Lose It?

GeroPsych ◽  
2010 ◽  
Vol 23 (3) ◽  
pp. 169-175 ◽  
Author(s):  
Adrian Schwaninger ◽  
Diana Hardmeier ◽  
Judith Riegelnig ◽  
Mike Martin
Keyword(s):  
Object Recognition ◽  
Cognitive Development ◽  
Recognition Test ◽  
Cognitive Aging ◽  
Recognition Task ◽  
Airport Security ◽  
Visual Object ◽  
Long Term Effects ◽  
Object Recognition Test ◽  
X Ray

In recent years, research on cognitive aging increasingly has focused on the cognitive development across middle adulthood. However, little is still known about the long-term effects of intensive job-specific training of fluid intellectual abilities. In this study we examined the effects of age- and job-specific practice of cognitive abilities on detection performance in airport security x-ray screening. In Experiment 1 (N = 308; 24–65 years), we examined performance in the X-ray Object Recognition Test (ORT), a speeded visual object recognition task in which participants have to find dangerous items in x-ray images of passenger bags; and in Experiment 2 (N = 155; 20–61 years) in an on-the-job object recognition test frequently used in baggage screening. Results from both experiments show high performance in older adults and significant negative age correlations that cannot be overcome by more years of job-specific experience. We discuss the implications of our findings for theories of lifespan cognitive development and training concepts.

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

BEHRT-HF: an interpretable transformer-based, deep learning model for prediction of incident heart failure

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Rao ◽  
Y Li ◽  
R Ramakrishnan ◽  
A Hassaine ◽  
D Canoy ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
State Of The Art ◽  
Failure Prediction ◽  
Predictive Performance ◽  
Learning Model ◽  
Learning Framework ◽  
Incident Heart Failure ◽  
Ablation Study ◽  
Deep Learning Model

Abstract Background/Introduction Predicting incident heart failure has been challenging. Deep learning models when applied to rich electronic health records (EHR) offer some theoretical advantages. However, empirical evidence for their superior performance is limited and they remain commonly uninterpretable, hampering their wider use in medical practice. Purpose We developed a deep learning framework for more accurate and yet interpretable prediction of incident heart failure. Methods We used longitudinally linked EHR from practices across England, involving 100,071 patients, 13% of whom had been diagnosed with incident heart failure during follow-up. We investigated the predictive performance of a novel transformer deep learning model, “Transformer for Heart Failure” (BEHRT-HF), and validated it using both an external held-out dataset and an internal five-fold cross-validation mechanism using area under receiver operating characteristic (AUROC) and area under the precision recall curve (AUPRC). Predictor groups included all outpatient and inpatient diagnoses within their temporal context, medications, age, and calendar year for each encounter. By treating diagnoses as anchors, we alternatively removed different modalities (ablation study) to understand the importance of individual modalities to the performance of incident heart failure prediction. Using perturbation-based techniques, we investigated the importance of associations between selected predictors and heart failure to improve model interpretability. Results BEHRT-HF achieved high accuracy with AUROC 0.932 and AUPRC 0.695 for external validation, and AUROC 0.933 (95% CI: 0.928, 0.938) and AUPRC 0.700 (95% CI: 0.682, 0.718) for internal validation. Compared to the state-of-the-art recurrent deep learning model, RETAIN-EX, BEHRT-HF outperformed it by 0.079 and 0.030 in terms of AUPRC and AUROC. Ablation study showed that medications were strong predictors, and calendar year was more important than age. Utilising perturbation, we identified and ranked the intensity of associations between diagnoses and heart failure. For instance, the method showed that established risk factors including myocardial infarction, atrial fibrillation and flutter, and hypertension all strongly associated with the heart failure prediction. Additionally, when population was stratified into different age groups, incident occurrence of a given disease had generally a higher contribution to heart failure prediction in younger ages than when diagnosed later in life. Conclusions Our state-of-the-art deep learning framework outperforms the predictive performance of existing models whilst enabling a data-driven way of exploring the relative contribution of a range of risk factors in the context of other temporal information. Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): National Institute for Health Research, Oxford Martin School, Oxford Biomedical Research Centre

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