CT-CAD: Context-Aware Transformers for End-to-End Chest Abnormality Detection on X-Rays

Kong, Qiran; Wu, Yirui; Yuan, Congying; Wang, Yongli

doi:10.1109/bibm52615.2021.9669743

Cited by 9 publications

(3 citation statements)

References 7 publications

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“…Thus, based on local and global image information, significant stenosis in coronary arteries can be accurately detected by TR-Net. Jiang et al [110] presented a caries detection framework based on YOLOv5s, where the transformer was incorporated in the backbone network to perform feature extraction from input data, and the FReLU activation function was adopted to the activation of visual-spatial information, which can achieve the accurate and efficient detection of caries. Kong et al [111] proposed an end-to-end abnormality detection model for chest X-ray images termed CT-CAD, which contains context-aware transformers.…”

Section: Transformer In Medical Image Detection Taskmentioning

confidence: 99%

Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds

Li²,

Yan³

et al. 2023

IJNDI

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Survey/review study Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds Xiang Li 1, Minglei Li 1, Pengfei Yan 1, Guanyi Li 1, Yuchen Jiang 1, Hao Luo 1,*, and Shen Yin 2 1 Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China 2 Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim 7034, Norway * Correspondence: hao.luo@hit.edu.cn Received: 16 October 2022 Accepted: 25 November 2022 Published: Abstract: With the improvement of hardware computing power and the development of deep learning algorithms, a revolution of "artificial intelligence (AI) + medical image" is taking place. Benefiting from diversified modern medical measurement equipment, a large number of medical images will be produced in the clinical process. These images improve the diagnostic accuracy of doctors, but also increase the labor burden of doctors. Deep learning technology is expected to realize an auxiliary diagnosis and improve diagnostic efficiency. At present, the method of deep learning technology combined with attention mechanism is a research hotspot and has achieved state-of-the-art results in many medical image tasks. This paper reviews the deep learning attention methods in medical image analysis. A comprehensive literature survey is first conducted to analyze the keywords and literature. Then, we introduce the development and technical characteristics of the attention mechanism. For its application in medical image analysis, we summarize the related methods in medical image classification, segmentation, detection, and enhancement. The remaining challenges, potential solutions, and future research directions are also discussed.

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Section: Transformer In Medical Image Detection Taskmentioning

confidence: 99%

Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds

Li²,

Yan³

et al. 2023

IJNDI

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“…Recently, transformer-based models, such as EEG-transformer, have been adept at processing variable-length sequences by employing a self-attention mechanism to capture dependencies between different segments. Nonetheless, these models come with higher computational costs and require large amounts of samples [46].…”

Section: Introductionmentioning

confidence: 99%

Kernel-Based Regularized EEGNet Using Centered Alignment and Gaussian Connectivity for Motor Imagery Discrimination

Tobón-Henao

Álvarez-Meza

Castellanos-Domínguez

2023

Computers

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Brain–computer interfaces (BCIs) from electroencephalography (EEG) provide a practical approach to support human–technology interaction. In particular, motor imagery (MI) is a widely used BCI paradigm that guides the mental trial of motor tasks without physical movement. Here, we present a deep learning methodology, named kernel-based regularized EEGNet (KREEGNet), leveled on centered kernel alignment and Gaussian functional connectivity, explicitly designed for EEG-based MI classification. The approach proactively tackles the challenge of intrasubject variability brought on by noisy EEG records and the lack of spatial interpretability within end-to-end frameworks applied for MI classification. KREEGNet is a refinement of the widely accepted EEGNet architecture, featuring an additional kernel-based layer for regularized Gaussian functional connectivity estimation based on CKA. The superiority of KREEGNet is evidenced by our experimental results from binary and multiclass MI classification databases, outperforming the baseline EEGNet and other state-of-the-art methods. Further exploration of our model’s interpretability is conducted at individual and group levels, utilizing classification performance measures and pruned functional connectivities. Our approach is a suitable alternative for interpretable end-to-end EEG-BCI based on deep learning.

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“…In addition, early identifying patients at risk of MI can prevent permanent cardiac damage using electrocardiogram (ECG) data to automatically and accurately predict myocardial infarction (MI) for detecting and treating heart diseases. Artificial intelligence-based deep learning (DL) and machine learning (ML) methods have achieved great success in the medical analysis domain [7][8][9][10]. There are many available AI techniques that use deep convolutional neural network (CNN) for the following reasons.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis Myocardial Infarction Based on Stacking Ensemble of Convolutional Neural Network

et al. 2022

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Artificial Intelligence (AI) technologies are vital in identifying patients at risk of serious illness by providing an early hazards risk. Myocardial infarction (MI) is a silent disease that has been harvested and is still threatening many lives. The aim of this work is to propose a stacking ensemble based on Convolutional Neural Network model (CNN). The proposed model consists of two primary levels, Level-1 and Level-2. In Level-1, the pre-trained CNN models (i.e., CNN-Model1, CNN-Model2, and CNN-Model3) produce the output probabilities and collect them in stacking for the training and testing sets. In Level-2, four meta-leaner classifiers (i.e., SVM, LR, RF, or KNN) are trained by stacking the output probabilities of the training set and are evaluated using the stacking of the output probabilities of the testing set to make the final prediction results. The proposed work was evaluated based on two ECG heartbeat signals datasets for MI: Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) and Physikalisch-Technische Bundesanstalt (PTB) datasets. The proposed model was compared with a diverse set of classical machine learning algorithms such as decision tree, K-nearest neighbor, and support vector machine, and the three base CNN classifiers of CNN-Model1, CNN-Model2, and CNN-Model3. The proposed model based on the RF meta-learner classifier obtained the highest scores, achieving remarkable results on both databases used. For the MIT-BIH dataset it achieved an accuracy of 99.8%, precision of 97%, recall of 96%, and F1-score of 94.4%, outperforming all other methods. while with PTB dataset achieved an accuracy of 99.7%, precision of 99%, recall of 99%, and F1-score of 99%, exceeding the other methods.

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CT-CAD: Context-Aware Transformers for End-to-End Chest Abnormality Detection on X-Rays

Cited by 9 publications

References 7 publications

Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds

Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds

Kernel-Based Regularized EEGNet Using Centered Alignment and Gaussian Connectivity for Motor Imagery Discrimination

Diagnosis Myocardial Infarction Based on Stacking Ensemble of Convolutional Neural Network

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