Privacy-preserving time series medical images analysis using a hybrid deep learning framework

Brain tumor is one of the leading causes of cancer-related death globally among children and adults. Precise classification of brain tumor grade (low-grade and high-grade glioma) at an early stage plays a key role in successful prognosis and treatment planning. With recent advances in deep learning, artificial intelligence–enabled brain tumor grading systems can assist radiologists in the interpretation of medical images within seconds. The performance of deep learning techniques is, however, highly depended on the size of the annotated dataset. It is extremely challenging to label a large quantity of medical images, given the complexity and volume of medical data. In this work, we propose a novel transfer learning–based active learning framework to reduce the annotation cost while maintaining stability and robustness of the model performance for brain tumor classification. In this retrospective research, we employed a 2D slice–based approach to train and fine-tune our model on the magnetic resonance imaging (MRI) training dataset of 203 patients and a validation dataset of 66 patients which was used as the baseline. With our proposed method, the model achieved area under receiver operating characteristic (ROC) curve (AUC) of 82.89% on a separate test dataset of 66 patients, which was 2.92% higher than the baseline AUC while saving at least 40% of labeling cost. In order to further examine the robustness of our method, we created a balanced dataset, which underwent the same procedure. The model achieved AUC of 82% compared with AUC of 78.48% for the baseline, which reassures the robustness and stability of our proposed transfer learning augmented with active learning framework while significantly reducing the size of training data.

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Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework

Remote Sensing ◽

10.3390/rs13091738 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1738

Author(s):

Huiyuan Fu ◽

Yuchao Zheng ◽

Yudong Ye ◽

Xueshang Feng ◽

Chaoxu Liu ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Arrival Time ◽

Optical Observations ◽

Feature Maps ◽

Feature Map ◽

Learning Framework ◽

Time Prediction ◽

Arrival Time Prediction ◽

Better Than

Fast and accurate prediction of the geoeffectiveness of coronal mass ejections (CMEs) and the arrival time of the geoeffective CMEs is urgent, to reduce the harm caused by CMEs. In this paper, we present a new deep learning framework based on time series of satellites’ optical observations that can give both the geoeffectiveness and the arrival time prediction of the CME events. It is the first time combining these two demands in a unified deep learning framework with no requirement of manually feature selection and get results immediately. The only input of the deep learning framework is the time series images from synchronized solar white-light and EUV observations. Our framework first uses the deep residual network embedded with the attention mechanism to extract feature maps for each observation image, then fuses the feature map of each image by the feature map fusion module and determines the geoeffectiveness of CME events. For the geoeffective CME events, we further predict its arrival time by the deep residual regression network based on group convolution. In order to train and evaluate our proposed framework, we collect 2400 partial-/full-halo CME events and its corresponding images from 1996 to 2018. The F1 score and Accuracy of the geoeffectiveness prediction can reach 0.270% and 75.1%, respectively, and the mean absolute error of the arrival time prediction is only 5.8 h, which are both significantly better than well-known deep learning methods and can be comparable to, or even better than, the best performance of traditional methods.

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A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM

Expert Systems with Applications ◽

10.1016/j.eswa.2020.113609 ◽

2020 ◽

Vol 159 ◽

pp. 113609

Author(s):

Yong'an Zhang ◽

Binbin Yan ◽

Memon Aasma

Keyword(s):

Time Series ◽

Deep Learning ◽

Financial Time Series ◽

Learning Framework ◽

Financial Time

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Privacy Preserving Deep Learning Framework in Fog Computing

Machine Learning, Optimization, and Data Science - Lecture Notes in Computer Science ◽

10.1007/978-3-030-64583-0_45 ◽

2020 ◽

pp. 504-515

Author(s):

Norma Gutiérrez ◽

Eva Rodríguez ◽

Sergi Mus ◽

Beatriz Otero ◽

Ramón Canal

Keyword(s):

Deep Learning ◽

Fog Computing ◽

Privacy Preserving ◽

Learning Framework

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