Significance:Early diagnosis of depression is crucial for effective treatment. Our study utilizes functional near-infrared spectroscopy (fNIRS) and machine learning to accurately classify mild and severe depression, providing an objective auxiliary diagnostic tool for mental health workers.Aim:Develop prediction models to distinguish between severe and mild depression using fNIRS data.Approach:We collected the fNIRS data from 140 subjects and applied a complete ensemble empirical mode decomposition with an adaptive noise-wavelet threshold combined denoising method (CEEMDAN-WPT) to remove noise during the verbal fluency task. The temporal features (TF) and correlation features (CF) from 18 prefrontal lobe channels of subjects were extracted as predictors. Using recursive feature elimination with cross-validation, we identified optimal TF or CF and examined their role in distinguishing between severe and mild depression. Machine learning algorithms were used for classification.Results:The combination of TF and CF as inputs for the prediction model yielded higher classification accuracy than using either TF or CF alone. Among the prediction models, the SVM-based model demonstrates excellent performance in nested cross-validation, achieving an accuracy rate of 92.8%.Conclusions:The proposed model can effectively distinguish mild depression from severe depression.

BACKGROUND:Colonic polyps are the most prevalent neoplastic lesions detected during colorectal cancer screening, and timely detection and excision of these precursor lesions is crucial for preventing multiple malignancies and reducing mortality rates.PURPOSE:The pressing need for intelligent polyp detection has led to the development of a high-precision intelligent polyp segmentation network designed to improve polyp screening rates during colonoscopies.METHODS:In this study, we employed ResNet50 as the backbone network and embedded a multi-channel grouping fusion encoding module in the third to fifth stages to extract high-level semantic features of polyps. Receptive field modules were utilized to capture multi-scale features, and grouping fusion modules were employed to capture salient features in different group channels, guiding the decoder to generate an initial global mapping with improved accuracy. To refine the segmentation of the initial global mapping, we introduced an enhanced boundary weight attention module that adaptively thresholds the initial global mapping using learnable parameters. A self-attention mechanism was then utilized to calculate the long-distance dependency relationship of the polyp boundary area, resulting in an output feature map with enhanced boundaries that effectively refines the boundary of the target area.RESULTS:We carried out contrast experiments of MGF-Net with mainstream polyp segmentation networks on five public datasets of ColonDB, CVC-ColonDB, CVC-612, Kvasir, and ETIS. The results demonstrate that the segmentation accuracy of MGF-Net is significantly improved on the datasets. Furthermore, a hypothesis test was conducted to assess the statistical significance of the computed results.CONCLUSIONS:Our proposed MGF-Net outperforms existing mainstream baseline networks and presents a promising solution to the pressing need for intelligent polyp detection. The proposed model is available at https://github.com/xiefanghhh/MGF-NET.

Background and objectiveComputer-based biomedical image segmentation plays a crucial role in planning of assisted diagnostics and therapy. However, due to the variable size and irregular shape of the segmentation target, it is still a challenge to construct an effective medical image segmentation structure. Recently, hybrid architectures based on convolutional neural networks (CNNs) and transformers were proposed. However, most current backbones directly replace one or all convolutional layers with transformer blocks, regardless of the semantic gap between features. Thus, how to sufficiently and effectively eliminate the semantic gap as well as combine the global and local information is a critical challenge.MethodsTo address the challenge, we propose a novel structure, called BiU-Net, which integrates CNNs and transformers with a two-stage fusion strategy. In the first fusion stage, called Single-Scale Fusion (SSF) stage, the encoding layers of the CNNs and transformers are coupled, with both having the same feature map size. The SSF stage aims to reconstruct local features based on CNNs and long-range information based on transformers in each encoding block. In the second stage, Multi-Scale Fusion (MSF), BiU-Net interacts with multi-scale features from various encoding layers to eliminate the semantic gap between deep and shallow layers. Furthermore, a Context-Aware Block (CAB) is embedded in the bottleneck to reinforce multi-scale features in the decoder.ResultsExperiments on four public datasets were conducted. On the BUSI dataset, our BiU-Net achieved 85.50% on Dice coefficient (Dice), 76.73% on intersection over union (IoU), and 97.23% on accuracy (ACC). Compared to the state-of-the-art method, BiU-Net improves Dice by 1.17%. For the Monuseg dataset, the proposed method attained the highest scores, reaching 80.27% and 67.22% for Dice and IoU. The BiU-Net achieves 95.33% and 81.22% Dice on the PH2 and DRIVE datasets.ConclusionsThe results of our experiments showed that BiU-Net transcends existing state-of-the-art methods on four publicly available biomedical datasets. Due to the powerful multi-scale feature extraction ability, our proposed BiU-Net is a versatile medical image segmentation framework for various types of medical images. The source code is released on (https://github.com/ZYLandy/BiU-Net).

Person re-identification aims to retrieve specific pedestrians from different cameras and scenes, in which extracting robust and discriminative features is crucial for this task. To explore the potential interactions among images and learn more robust representations, this paper proposes Transformer-based Feature Interactor(TFI) and improved Margin Self-punishment Softmax loss(MS-Softamx). The Transformer-based Feature Interactor consists of Group Channel Pyramid Attention(GCPA) and Neighbor Interaction Modeling(NIM). Firstly, the Group Channel Pyramid Attention module provides prior information for high-level semantics via low-level semantics. The attention information is gradually stacked from coarse to fine to obtain enhanced hierarchical multi-scale features. Then, Neighbor Interaction Modeling effectively model the input and similar neighbors to produce a more robust and discriminative image representation. To make TFI more focused on intra-class embedding learning, we also propose Margin Self-punishment Softmax guide deep network learning, which obtains a tighter custom classification boundary by pushing the inter-class threshold and minimizing the intra-class variance. The proposed method is verified on four datasets, and this achieves 92.8%/95.6% mAP/Rank-1 on Market1501, 86.1%/90.8% mAP/Rank-1 on DukeMTMC, 64.4%/ 81.2% mAP/Rank-1 on MSMT17, 79.7%/80.8% mAP/Rank-1 on CUHK03-detected and 81.8%/81.9% mAP/Rank-1 on CUHK03-labeled. Extensive experiments demonstrate that the proposed method achieves competitive performance with other state-of-the-art methods.

This paper proposed a method based on heart rate variability (HRV) for evaluating the accuracy of wearable devices in measuring heart rate. HRV refers to the variation in time intervals between successive heartbeats, widely used in many fields such as clinical and sports fields. Wearable devices such as Electrocardiogram (ECG) electrode patches have gained popularity due to their portability and ease of use. However, they can be prone to measurement interference caused by environmental noise, human respiration, etc. The proposed method consists of four main components: selection of "gold standard measurement devices", identification of HRV measurement metrics, construction of an HRV evaluation framework, and quantification of measurement errors. The method is validated through simulated experiments using ECG patches. The evaluation framework and quantification model established in this method have significant implications in establishment of industry standards and diagnosis of diseases in clinical practice.

In recent years, brain magnetic resonance imaging (MRI) image segmentation has drawn considerable attention. MRI image segmentation result provides a basis for medical diagnosis. The segmentation result influences the clinical treatment directly. Nevertheless, MRI images have shortcomings such as noise and the inhomogeneity of grayscale. The performance of traditional segmentation algorithms still needs further improvement. In this paper, we propose a novel brain MRI image segmentation algorithm based on fuzzy C-means (FCM) clustering algorithm to improve the segmentation accuracy. First, we introduce multitask learning strategy into FCM to extract public information among different segmentation tasks. It combines the advantages of the two algorithms. The algorithm enables to utilize both public information among different tasks and individual information within tasks. Then, we design an adaptive task weight learning mechanism, and a weighted multitask fuzzy C-means (WMT-FCM) clustering algorithm is proposed. Under the adaptive task weight learning mechanism, each task obtains the optimal weight and achieves better clustering performance. Simulated MRI images from McConnell BrainWeb have been used to evaluate the proposed algorithm. Experimental results demonstrate that the proposed method provides more accurate and stable segmentation results than its competitors on the MRI images with various noise and intensity inhomogeneity.

Due to the fast-paced lifestyle, individuals may experience varying degrees of depression attributed to stress, academic pursuits, and other causative factors. Hemodynamics and functional connectivity (correlation between channels) of the prefrontal lobe have been identified as crucial factors in assessing the severity of depression. As a non-invasive technique for monitoring cerebral blood flow, functional near-infrared spectroscopy (fNIRS) shows promising potential as a tool for objective auxiliary diagnosis of depression.This study aimed to develop prediction models for distinguishing patients with severe depression from those with mild depression based on the dataset collected by fNIRS.We collected the fNIRS data from 140 subjects, and used a complete ensemble empirical mode decomposition with adaptive noise-wavelet threshold combined denoising method (CEEMDAN-WPT) to remove the jitter and artefact noise generated during the verbal fluency task (VFT). The temporal and correlation features of 18 channels in the prefrontal lobe of the subjects were extracted as predictors. We screened out the optimal temporal features (TF) or correlation features (CF) using the RFECV, and investigated their role in distinguishing severe and mild depression, respectively. The fusion of TF and CF, as the input of the prediction model, yielded higher classification accuracy than using TF or CF alone as the prediction factor. Among the prediction models, the SVM-based predictive model performed well in nested cross-validation, with an accuracy rate of 92.8%.The proposed model effectively distinguishes mild depression from severe depression, provides an objective diagnostic method for mental health workers, and is significant in treating patients with depression.

Abstract Adenomatous polyps are usually the most common tumor in clinical screening for colorectal cancer. Early detection and removal of these precursor lesions have been proved to be effective in preventing many cancers and reducing the mortality rate. Therefore, intelligent polyp detection is an urgent matter, which could assist clinicians to quickly identify detected polyps at an early stage. This paper proposes a polyp segmentation method based on sequential self-attentive networks with the features such as the color and texture of images and the relationship features between them. The color and texture features of the region image are extracted and fused with the original image. The resulting feature maps are serially input to the Transformer model to learn high-level spatial and attentional features. The loss of spatial information is reduced by multiscale local aggregation. By aggregating the models from a global perspective, the transformer model can further reduce the loss of context information caused by deep convolution. Controlled experiments between the PAFormer network and the mainstream polyp segmentation networks are conducted on five datasets, including ColonDB, ETIS, CVC-ColonDB, CVC-612 and Kvasir. The results show that the segmentation accuracy of PAFormer has great improvement on the datasets and PAFormer is superior to existing mainstream baseline networks.