Classification Method of ECG Signals Based on RANet.

BACKGROUND: Electrocardiograms (ECG) are an important source of information on human heart health and are widely used to detect different types of arrhythmias. OBJECTIVE: With the advancement of deep learning, end-to-end ECG classification models based on neural networks have been developed. However, deeper network layers lead to gradient vanishing. Moreover, different channels and periods of an ECG signal hold varying significance for identifying different types of ECG abnormalities. METHODS: To solve these two problems, an ECG classification method based on a residual attention neural network is proposed in this paper. The residual network (ResNet) is used to solve the gradient vanishing problem. Moreover, it has fewer model parameters, and its structure is simpler. An attention mechanism is added to focus on key information, integrate channel features, and improve voting methods to alleviate the problem of data imbalance. RESULTS: Experiments and verifications are conducted using the PhysioNet/CinC Challenge 2017 dataset. The average F1 value is 0.817, which is 0.064 higher than that for the ResNet model. Compared with the mainstream methods, the performance is excellent.

A cGAN-based tumor segmentation method for breast ultrasound images.

Objective. This paper proposes a conditional GAN (cGAN)-based method to perform data enhancement of ultrasound images and segmentation of tumors in breast ultrasound images, which improves the reality of the enhenced breast ultrasound image and obtains a more accurate segmentation result.Approach. We use the idea of generative adversarial training to accomplish the following two tasks: (1) in this paper, we use generative adversarial networks to generate a batch of samples with labels from the perspective of label-generated images to expand the dataset from a data enhancement perspective. (2) In this paper, we use adversarial training instead of postprocessing steps such as conditional random fields to enhance higher-level spatial consistency. In addition, this work proposes a new network, EfficientUNet, based on U-Net, which combines ResNet18, an attention mechanism and a deep supervision technique. This segmentation model uses the residual network as an encoder to retain the lost information in the original encoder and can avoid the gradient disappearance problem to improve the feature extraction ability of the model, and it also uses deep supervision techniques to speed up the convergence of the model. The channel-by-channel weighting module of SENet is then used to enable the model to capture the tumor boundary more accurately.Main results. The paper concludes with experiments to verify the validity of these efforts by comparing them with mainstream methods on Dataset B. The Dice score and IoU score reaches 0.8856 and 0.8111, respectively.Significance. This study successfully combines cGAN and optimized EfficientUNet for the segmentation of breast tumor ultrasound images. The conditional generative adversarial network has a good performance in data enhancement, and the optimized EfficientUNet makes the segmentation more accurate.

Non-invasive Assessment of Axillary Lymph Node Metastasis Risk in Early Invasive Breast Cancer Adopting Automated Breast Volume Scanning-Based Radiomics Nomogram: A Multicenter Study.

OBJECTIVE: The aim of the work described here was to develop a non-invasive tool based on the radiomics and ultrasound features of automated breast volume scanning (ABVS), clinicopathological factors and serological indicators to evaluate axillary lymph node metastasis (ALNM) in patients with early invasive breast cancer (EIBC). METHODS: We retrospectively analyzed 179 ABVS images of patients with EIBC at a single center from January 2016 to April 2022 and divided the patients into training and validation sets (ratio 8:2). Additionally, 97 ABVS images of patients with EIBC from a second center were enrolled as the test set. The radiomics signature was established with the least absolute shrinkage and selection operator. Significant ALNM predictors were screened using univariate logistic regression analysis and further combined to construct a nomogram using the multivariate logistic regression model. The receiver operating characteristic curve assessed the nomogram's predictive performance. DISCUSSION: The constructed radiomics nomogram model, including ABVS radiomics signature, ultrasound assessment of axillary lymph node (ALN) status, convergence sign and erythrocyte distribution width (standard deviation), achieved moderate predictive performance for risk probability evaluation of ALNs in patients with EIBC. Compared with ultrasound, the nomogram model was able to provide a risk probability evaluation tool not only for the ALNs with positive ultrasound features but also for micrometastatic ALNs (generally without positive ultrasound features), which benefited from the radiomics analysis of multi-sourced data of patients with EIBC. CONCLUSION: This ABVS-based radiomics nomogram model is a pre-operative, non-invasive and visualized tool that can help clinicians choose rational diagnostic and therapeutic protocols for ALNM.

Quantitative analysis of near infrared spectroscopic data based on dual-band transformation and competitive adaptive reweighted sampling.

Near infrared (NIR) spectroscopy has the characteristics of rapid processing, nondestructive analysis and on-line detection. This technique has been widely used in the fields of quantitative determination and substance content analysis. However, for complex NIR spectral data, most traditional machine learning models cannot carry out effective quantitative analyses (manifested as underfitting; that is, the training effect of the model is not good). Small amounts of available data limit the performance of deep learning-based infrared spectroscopy methods, while the traditional threshold-based feature selection methods require more prior knowledge. To address the above problems, this paper proposes a competitive adaptive reweighted sampling method based on dual band transformation (DWT-CARS). DWT-CARS includes four types in total: CARS based on integrated two-dimensional correlation spectrum (i2DCOS-CARS), CARS based on difference coefficient (DI-CARS), CARS based on ratio coefficient (RI-CARS) and CARS based on normalized difference coefficient (NDI-CARS). We conducted comparative experiments on three datasets; compared to traditional machine learning methods, our method achieved good results, demonstrating that this method has considerable prospects for the quantitative analysis of near-infrared spectroscopic data. To further improve the performance and stability of this method, we combined the idea of integrated modeling and constructed a partial least squares model based on Monte Carlo sampling for the samples obtained by CARS (DWT-CARS-MC-PLS). Through comparative experiments, we verified that the integrated model could further enhance the accuracy and stability of the results.

An Optimized Radiomics Model Based on Automated Breast Volume Scan Images to Identify Breast Lesions: Comparison of Machine Learning Methods: Comparison of Machine Learning Methods.

OBJECTIVES: To develop and test an optimized radiomics model based on multi-planar automated breast volume scan (ABVS) images to identify malignant and benign breast lesions. METHODS: Patients (n = 200) with breast lesions who underwent ABVS examinations were included. For each patient, 208 radiomics features were extracted from the ABVS images, including axial plane and coronal plane. Recursive feature elimination, random forest, and chi-square test were used to select features. A support vector machine, logistic regression, and extreme gradient boosting were utilized as classifiers to differentiate malignant and benign breast lesions. The area under the curve, sensitivity, specificity, accuracy, and precision was used to evaluate the performance of the radiomics models. Generalization of the radiomics models was verified through 5-fold cross-validation. RESULTS: For a single plane or a combination of planes, a combination of recursive feature elimination, and support vector machine yielded the best performance when identifying breast lesions. The machine learning models based on a combination of planes performed better than those based on a single plane. Regarding the axial plane and coronal plane, the machine learning model using a combination of recursive feature elimination and support vector machine yielded the optimal identification performance: average area under the curve (0.857 ± 0.058, 95% confidence interval, 0.763-0.957); the average values of sensitivity, specificity, accuracy, and precision were 87.9, 68.2, 80.7, and 82.9%, respectively. CONCLUSIONS: The optimized radiomics model based on ABVS images can provide valuable information for identifying benign and malignant breast lesions preoperatively and guide the accurate clinical treatment. Further external validation is required.

Liver tumor detection based on objects as points.

The automatic detection of liver tumors by computed tomography is challenging, owing to their wide variations in size and location, as well as to their irregular shapes. Existing detection methods largely rely on two-stage detectors and use CT images marked with bounding boxes for training and detection. In this study, we propose a single-stage detector method designed to accurately detect multiple tumors simultaneously, and provide results demonstrating its increased speed and efficiency compared to prior methods. The proposed model divides CT images into multiple channels to obtain continuity information and implements a bounding box attention mechanism to overcome the limitation of inaccurate prediction of tumor center points and decrease redundant bounding boxes. The model integrates information from various channels using an effective Squeeze-and-Excitation attention module. The proposed model obtained a mean average precision result of 0.476 on the Decathlon dataset, which was superior to that of the prior methods examined for comparison. This research is expected to enable physicians to diagnose tumors very efficiently; particularly, the prediction of tumor center points is expected to enable physicians to rapidly verify their diagnostic judgments. The proposed method is considered suitable for future adoption in clinical practice in hospitals and resource-poor areas because its superior performance does not increase computational cost; hence, the equipment required is relatively inexpensive.

An Effective Dual Self-Attention Residual Network for Seizure Prediction.

As one of the most challenging data analysis tasks in chronic brain diseases, epileptic seizure prediction has attracted extensive attention from many researchers. Seizure prediction, can greatly improve patients' quality of life in many ways, such as preventing accidents and reducing harm that may occur during epileptic seizures. This work aims to develop a general method for predicting seizures in specific patients through exploring the time-frequency correlation of features obtained from multi-channel EEG signals. We convert the original EEG signals into spectrograms that represent time-frequency characteristics by applying short-time Fourier transform (STFT) to the EEG signals. For the first time, we propose a dual self-attention residual network (RDANet) that combines a spectrum attention module integrating local features with global features, with a channel attention module mining the interdependence between channel mappings to achieve better forecasting performance. Our proposed approach achieved a sensitivity of 89.33%, a specificity of 93.02%, an AUC of 91.26% and an accuracy of 92.07% on 13 patients from the public CHB-MIT scalp EEG dataset. Our experiments show that different EEG signal prediction segment lengths are an important factor affecting prediction performance. Our proposed method is competitive and achieves good robustness without patient-specific engineering.

Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts.

BACKGROUND: Bronchoscopy for suspected lung cancer has low diagnostic sensitivity, rendering many inconclusive results. The Bronchial Genomic Classifier (BGC) was developed to help with patient management by identifying those with low risk of lung cancer when bronchoscopy is inconclusive. The BGC was trained and validated on patients in the Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer (AEGIS) trials. A modern patient cohort, the BGC Registry, showed differences in key clinical factors from the AEGIS cohorts, with less smoking history, smaller nodules and older age. Additionally, we discovered interfering factors (inhaled medication and sample collection timing) that impacted gene expressions and potentially disguised genomic cancer signals. METHODS: In this study, we leveraged multiple cohorts and next generation sequencing technology to develop a robust Genomic Sequencing Classifier (GSC). To address demographic composition shift and interfering factors, we synergized three algorithmic strategies: 1) ensemble of clinical dominant and genomic dominant models; 2) development of hierarchical regression models where the main effects from clinical variables were regressed out prior to the genomic impact being fitted in the model; and 3) targeted placement of genomic and clinical interaction terms to stabilize the effect of interfering factors. The final GSC model uses 1232 genes and four clinical covariates - age, pack-years, inhaled medication use, and specimen collection timing. RESULTS: In the validation set (N = 412), the GSC down-classified low and intermediate pre-test risk subjects to very low and low post-test risk with a specificity of 45% (95% CI 37-53%) and a sensitivity of 91% (95%CI 81-97%), resulting in a negative predictive value of 95% (95% CI 89-98%). Twelve percent of intermediate pre-test risk subjects were up-classified to high post-test risk with a positive predictive value of 65% (95%CI 44-82%), and 27% of high pre-test risk subjects were up-classified to very high post-test risk with a positive predictive value of 91% (95% CI 78-97%). CONCLUSIONS: The GSC overcame the impact of interfering factors and achieved consistent performance across multiple cohorts. It demonstrated diagnostic accuracy in both down- and up-classification of cancer risk, providing physicians actionable information for many patients with inconclusive bronchoscopy.

Optical frequency comb based multi-band microwave frequency conversion for satellite applications.

Based on optical frequency combs (OFC), we propose an efficient and flexible multi-band frequency conversion scheme for satellite repeater applications. The underlying principle is to mix dual coherent OFCs with one of which carrying the input signal. By optically channelizing the mixed OFCs, the converted signal in different bands can be obtained in different channels. Alternatively, the scheme can be configured to generate multi-band local oscillators (LO) for widely distribution. Moreover, the scheme realizes simultaneous inter- and intra-band frequency conversion just in a single structure and needs only three frequency-fixed microwave sources. We carry out a proof of concept experiment in which multiple LOs with 2 GHz, 10 GHz, 18 GHz, and 26 GHz are generated. A C-band signal of 6.1 GHz input to the proposed scheme is successfully converted to 4.1 GHz (C band), 3.9 GHz (C band) and 11.9 GHz (X band), etc. Compared with the back-to-back (B2B) case measured at 0 dBm input power, the proposed scheme shows a 9.3% error vector magnitude (EVM) degradation at each output channel. Furthermore, all channels satisfy the EVM limit in a very wide input power range.