Breast cancer is a malignancy caused by the abnormal proliferation of breast epithelial cells, predominantly affecting female patients, and it is commonly diagnosed using histopathological images. Currently, deep learning techniques have made significant breakthroughs in medical image processing, outperforming traditional detection methods in breast cancer pathology classification tasks. This paper first reviewed the advances in applying deep learning to breast pathology images, focusing on three key areas: multi-scale feature extraction, cellular feature analysis, and classification. Next, it summarized the advantages of multimodal data fusion methods for breast pathology images. Finally, the study discussed the challenges and future prospects of deep learning in breast cancer pathology image diagnosis, providing important guidance for advancing the use of deep learning in breast diagnosis.
Glioma is a primary brain tumor with high incidence rate. High-grade gliomas (HGG) are those with the highest degree of malignancy and the lowest degree of survival. Surgical resection and postoperative adjuvant chemoradiotherapy are often used in clinical treatment, so accurate segmentation of tumor-related areas is of great significance for the treatment of patients. In order to improve the segmentation accuracy of HGG, this paper proposes a multi-modal glioma semantic segmentation network with multi-scale feature extraction and multi-attention fusion mechanism. The main contributions are, (1) Multi-scale residual structures were used to extract features from multi-modal gliomas magnetic resonance imaging (MRI); (2) Two types of attention modules were used for features aggregating in channel and spatial; (3) In order to improve the segmentation performance of the whole network, the branch classifier was constructed using ensemble learning strategy to adjust and correct the classification results of the backbone classifier. The experimental results showed that the Dice coefficient values of the proposed segmentation method in this article were 0.909 7, 0.877 3 and 0.839 6 for whole tumor, tumor core and enhanced tumor respectively, and the segmentation results had good boundary continuity in the three-dimensional direction. Therefore, the proposed semantic segmentation network has good segmentation performance for high-grade gliomas lesions.
High resolution (HR) magnetic resonance images (MRI) or computed tomography (CT) images can provide clearer anatomical details of human body, which facilitates early diagnosis of the diseases. However, due to the imaging system, imaging environment and human factors, it is difficult to obtain clear high-resolution images. In this paper, we proposed a novel medical image super resolution (SR) reconstruction method via multi-scale information distillation (MSID) network in the non-subsampled shearlet transform (NSST) domain, namely NSST-MSID network. We first proposed a MSID network that mainly consisted of a series of stacked MSID blocks to fully exploit features from images and effectively restore the low resolution (LR) images to HR images. In addition, most previous methods predict the HR images in the spatial domain, producing over-smoothed outputs while losing texture details. Thus, we viewed the medical image SR task as the prediction of NSST coefficients, which make further MSID network keep richer structure details than that in spatial domain. Finally, the experimental results on our constructed medical image datasets demonstrated that the proposed method was capable of obtaining better peak signal to noise ratio (PSNR), structural similarity (SSIM) and root mean square error (RMSE) values and keeping global topological structure and local texture detail better than other outstanding methods, which achieves good medical image reconstruction effect.
The diagnosis of hypertrophic cardiomyopathy (HCM) is of great significance for the early risk classification of sudden cardiac death and the screening of family genetic diseases. This research proposed a HCM automatic detection method based on convolution neural network (CNN) model, using single-lead electrocardiogram (ECG) signal as the research object. Firstly, the R-wave peak locations of single-lead ECG signal were determined, followed by the ECG signal segmentation and resample in units of heart beats, then a CNN model was built to automatically extract the deep features in the ECG signal and perform automatic classification and HCM detection. The experimental data is derived from 108 ECG records extracted from three public databases provided by PhysioNet, the database established in this research consists of 14,459 heartbeats, and each heartbeat contains 128 sampling points. The results revealed that the optimized CNN model could effectively detect HCM, the accuracy, sensitivity and specificity were 95.98%, 98.03% and 95.79% respectively. In this research, the deep learning method was introduced for the analysis of single-lead ECG of HCM patients, which could not only overcome the technical limitations of conventional detection methods based on multi-lead ECG, but also has important application value for assisting doctor in fast and convenient large-scale HCM preliminary screening.
The processing mechanism of the human brain for speech information is a significant source of inspiration for the study of speech enhancement technology. Attention and lateral inhibition are key mechanisms in auditory information processing that can selectively enhance specific information. Building on this, the study introduces a dual-branch U-Net that integrates lateral inhibition and feedback-driven attention mechanisms. Noisy speech signals input into the first branch of the U-Net led to the selective feedback of time-frequency units with high confidence. The generated activation layer gradients, in conjunction with the lateral inhibition mechanism, were utilized to calculate attention maps. These maps were then concatenated to the second branch of the U-Net, directing the network’s focus and achieving selective enhancement of auditory speech signals. The evaluation of the speech enhancement effect was conducted by utilising five metrics, including perceptual evaluation of speech quality. This method was compared horizontally with five other methods: Wiener, SEGAN, PHASEN, Demucs and GRN. The experimental results demonstrated that the proposed method improved speech signal enhancement capabilities in various noise scenarios by 18% to 21% compared to the baseline network across multiple performance metrics. This improvement was particularly notable in low signal-to-noise ratio conditions, where the proposed method exhibited a significant performance advantage over other methods. The speech enhancement technique based on lateral inhibition and feedback-driven attention mechanisms holds significant potential in auditory speech enhancement, making it suitable for clinical practices related to artificial cochleae and hearing aids.
Manual segmentation of coronary arteries in computed tomography angiography (CTA) images is inefficient, and existing deep learning segmentation models often exhibit low accuracy on coronary artery images. Inspired by the Transformer architecture, this paper proposes a novel segmentation model, the double parallel encoder u-net with transformers (DUNETR). This network employed a dual-encoder design integrating Transformers and convolutional neural networks (CNNs). The Transformer encoder transformed three-dimensional (3D) coronary artery data into a one-dimensional (1D) sequential problem, effectively capturing global multi-scale feature information. Meanwhile, the CNN encoder extracted local features of the 3D coronary arteries. The complementary features extracted by the two encoders were fused through the noise reduction feature fusion (NRFF) module and passed to the decoder. Experimental results on a public dataset demonstrated that the proposed DUNETR model achieved a Dice similarity coefficient of 81.19% and a recall rate of 80.18%, representing improvements of 0.49% and 0.46%, respectively, over the next best model in comparative experiments. These results surpassed those of other conventional deep learning methods. The integration of Transformers and CNNs as dual encoders enables the extraction of rich feature information, significantly enhancing the effectiveness of 3D coronary artery segmentation. Additionally, this model provides a novel approach for segmenting other vascular structures.
To address the issues of information imbalance and the difficulty in synergistically extracting global and local features during extracorporeal membrane oxygenation (ECMO) fault diagnosis, this paper proposes a model integrating Chebyshev graph convolutional neural networks (ChebyNet) with convolutional neural networks (CNN) (CNN-ChebyNet). This model is applied to ECMO fault diagnosis tasks to efficiently enhance feature extraction accuracy. Firstly, a graph symmetry processing mechanism is introduced into the ChebyNet framework to improve the balance of information flow between nodes. Secondly, by combining ChebyNet’s global modeling capability with CNN’s local temporal feature extraction ability, multidimensional representation of complex fault features is achieved. Finally, through multi-task learning node reconstruction and classification tasks, the perception of latent correlations among samples is enhanced. Experiments on the ECMO blood pump impeller assembly dataset demonstrate that the CNN-ChebyNet model achieves superior performance across multiple comparison methods, with an average diagnostic accuracy exceeding 99%, showcasing outstanding diagnostic capability and stability. Furthermore, ablation experiments validate the effectiveness of each model component in multi-fault identification. In summary, this study provides an effective and feasible technical solution for fault diagnosis in ECMO devices.
Macaque is a common animal model in drug safety assessment. Its behavior reflects its health condition before and after drug administration, which can effectively reveal the side effects of drugs. At present, researchers usually rely on artificial methods to observe the behavior of macaque, which cannot achieve uninterrupted 24-hour monitoring. Therefore, it is urgent to develop a system to realize 24-hour observation and recognition of macaque behavior. In order to solve this problem, this paper constructs a video dataset containing nine kinds of macaque behaviors (MBVD-9), and proposes a network called Transformer-augmented SlowFast for macaque behavior recognition (TAS-MBR) based on this dataset. Specifically, the TAS-MBR network converts the red, green and blue (RGB) color mode frame input by its fast branches into residual frames on the basis of SlowFast network and introduces the Transformer module after the convolution operation to obtain sports information more effectively. The results show that the average classification accuracy of TAS-MBR network for macaque behavior is 94.53%, which is significantly improved compared with the original SlowFast network, proving the effectiveness and superiority of the proposed method in macaque behavior recognition. This work provides a new idea for the continuous observation and recognition of the behavior of macaque, and lays the technical foundation for the calculation of monkey behaviors before and after medication in drug safety evaluation.
Three-dimensional (3D) deformable image registration plays a critical role in 3D medical image processing. This technique aligns images from different time points, modalities, or individuals in 3D space, enabling the comparison and fusion of anatomical or functional information. To simultaneously capture the local details of anatomical structures and the long-range dependencies in 3D medical images, while reducing the high costs of manual annotations, this paper proposes an unsupervised 3D medical image registration method based on shifted window Transformer and convolutional neural network (CNN), termed Swin Transformer-CNN-hybrid network (STCHnet). In the encoder part, STCHnet uses Swin Transformer and CNN to extract global and local features from 3D images, respectively, and optimizes feature representation through feature fusion. In the decoder part, STCHnet utilizes Swin Transformer to integrate information globally, and CNN to refine local details, reducing the complexity of the deformation field while maintaining registration accuracy. Experiments on the information extraction from images (IXI) and open access series of imaging studies (OASIS) datasets, along with qualitative and quantitative comparisons with existing registration methods, demonstrate that the proposed STCHnet outperforms baseline methods in terms of Dice similarity coefficient (DSC) and standard deviation of the log-Jacobian determinant (SDlogJ), achieving improved 3D medical image registration performance under unsupervised conditions.
Aiming at the problem that the unbalanced distribution of data in sleep electroencephalogram(EEG) signals and poor comfort in the process of polysomnography information collection will reduce the model's classification ability, this paper proposed a sleep state recognition method using single-channel EEG signals (WKCNN-LSTM) based on one-dimensional width kernel convolutional neural networks(WKCNN) and long-short-term memory networks (LSTM). Firstly, the wavelet denoising and synthetic minority over-sampling technique-Tomek link (SMOTE-Tomek) algorithm were used to preprocess the original sleep EEG signals. Secondly, one-dimensional sleep EEG signals were used as the input of the model, and WKCNN was used to extract frequency-domain features and suppress high-frequency noise. Then, the LSTM layer was used to learn the time-domain features. Finally, normalized exponential function was used on the full connection layer to realize sleep state. The experimental results showed that the classification accuracy of the one-dimensional WKCNN-LSTM model was 91.80% in this paper, which was better than that of similar studies in recent years, and the model had good generalization ability. This study improved classification accuracy of single-channel sleep EEG signals that can be easily utilized in portable sleep monitoring devices.