A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

2019 ◽  
Vol 36 (6) ◽  
Author(s):  
Ahmad Hassanpour ◽  
Majid Moradikia ◽  
Hojjat Adeli ◽  
Seyed Raouf Khayami ◽  
Pirooz Shamsinejadbabaki
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Learning Scheme ◽  
End To End

scholarly journals A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 202100-202110
Author(s):  
Jun Yang ◽  
Zhengmin Ma ◽  
Jin Wang ◽  
Yunfa Fu
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Spatial Representation ◽  
Learning Scheme

A novel hybrid deep learning scheme for four-class motor imagery classification

2019 ◽  
Vol 16 (6) ◽  
pp. 066004 ◽  
Author(s):  
Ruilong Zhang ◽  
Qun Zong ◽  
Liqian Dou ◽  
Xinyi Zhao
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Learning Scheme

scholarly journals Matching Large Baseline Oblique Stereo Images Using an End-to-End Convolutional Neural Network

2021 ◽  
Vol 13 (2) ◽  
pp. 274
Author(s):  
Guobiao Yao ◽  
Alper Yilmaz ◽  
Li Zhang ◽  
Fei Meng ◽  
Haibin Ai ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Stereo Matching ◽  
Least Square ◽  
Affine Invariant ◽  
Stereo Images ◽  
Distance Ratio ◽  
Matching Algorithm ◽  
End To End

The available stereo matching algorithms produce large number of false positive matches or only produce a few true-positives across oblique stereo images with large baseline. This undesired result happens due to the complex perspective deformation and radiometric distortion across the images. To address this problem, we propose a novel affine invariant feature matching algorithm with subpixel accuracy based on an end-to-end convolutional neural network (CNN). In our method, we adopt and modify a Hessian affine network, which we refer to as IHesAffNet, to obtain affine invariant Hessian regions using deep learning framework. To improve the correlation between corresponding features, we introduce an empirical weighted loss function (EWLF) based on the negative samples using K nearest neighbors, and then generate deep learning-based descriptors with high discrimination that is realized with our multiple hard network structure (MTHardNets). Following this step, the conjugate features are produced by using the Euclidean distance ratio as the matching metric, and the accuracy of matches are optimized through the deep learning transform based least square matching (DLT-LSM). Finally, experiments on Large baseline oblique stereo images acquired by ground close-range and unmanned aerial vehicle (UAV) verify the effectiveness of the proposed approach, and comprehensive comparisons demonstrate that our matching algorithm outperforms the state-of-art methods in terms of accuracy, distribution and correct ratio. The main contributions of this article are: (i) our proposed MTHardNets can generate high quality descriptors; and (ii) the IHesAffNet can produce substantial affine invariant corresponding features with reliable transform parameters.

Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review

2021 ◽  
Author(s):  
Hamdi Altaheri ◽  
Ghulam Muhammad ◽  
Mansour Alsulaiman ◽  
Syed Umar Amin ◽  
Ghadir Ali Altuwaijri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Learning Techniques ◽  
Electroencephalogram Eeg

scholarly journals Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kwang-Hyun Uhm ◽  
Seung-Won Jung ◽  
Moon Hyung Choi ◽  
Hong-Kyu Shin ◽  
Jae-Ik Yoo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Kidney Cancer ◽  
Renal Tumor ◽  
Renal Tumors ◽  
Abdominal Computed Tomography ◽  
Wide Range ◽  
End To End ◽  
Multi Phase ◽  
Histologic Subtypes

AbstractIn 2020, it is estimated that 73,750 kidney cancer cases were diagnosed, and 14,830 people died from cancer in the United States. Preoperative multi-phase abdominal computed tomography (CT) is often used for detecting lesions and classifying histologic subtypes of renal tumor to avoid unnecessary biopsy or surgery. However, there exists inter-observer variability due to subtle differences in the imaging features of tumor subtypes, which makes decisions on treatment challenging. While deep learning has been recently applied to the automated diagnosis of renal tumor, classification of a wide range of subtype classes has not been sufficiently studied yet. In this paper, we propose an end-to-end deep learning model for the differential diagnosis of five major histologic subtypes of renal tumors including both benign and malignant tumors on multi-phase CT. Our model is a unified framework to simultaneously identify lesions and classify subtypes for the diagnosis without manual intervention. We trained and tested the model using CT data from 308 patients who underwent nephrectomy for renal tumors. The model achieved an area under the curve (AUC) of 0.889, and outperformed radiologists for most subtypes. We further validated the model on an independent dataset of 184 patients from The Cancer Imaging Archive (TCIA). The AUC for this dataset was 0.855, and the model performed comparably to the radiologists. These results indicate that our model can achieve similar or better diagnostic performance than radiologists in differentiating a wide range of renal tumors on multi-phase CT.

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