To improve the effectiveness and efficiency of biomedical information retrieval by proposing ranking-based methods for selecting an optimal subset of retrieval systems for data fusion, we propose three ranking-based subset selection methods SFS (Sequential Forward Search), D&P (Diversity & Performance), and P&D (Performance & Diversity). These methods were applied in combination with the Reciprocal Rank Fusion technique. Experiments were conducted on four medical datasets from TREC, using between 62 and 125 candidate retrieval systems, and selecting up to 15 for fusion. The proposed subset selection methods significantly improved retrieval performance. Fusing the selected systems using RRF yielded improvements ranging from 10% to over 60% compared to the best individual retrieval system across the datasets. They also outperform the state-of-the-art technology by a large margin. In summary, our subset selection approach offers a practical and cost-efficient solution for biomedical information retrieval, achieving substantial performance gains while reducing computational overhead.

ABSTRACT In industrial product manufacturing, the automated detection and localisation of surface defects are of significant importance for ensuring quality control. However, existing computer vision‐based defect detection methods struggle to achieve both lightweight design and high accuracy on resource‐constrained embedded platforms, which limits their application in practical industrial detection environments. To address this issue, we propose DSGNet, a lightweight surface defect segmentation model, which serves as a core defect detection and localisation method for industrial inspection systems. The proposed model adopts an asymmetric encoder‐decoder structure to simplify the overall architecture. We designed an efficient feature extraction network by using four lightweight feature extraction units based on efficient convolutions. Furthermore, we introduce a hierarchical adaptive upsampling fusion (HAFU) mechanism and a lightweight bidirectional multiscale strip attention (LBMSA) feature refinement module to effectively fuse and refine the multilevel features extracted from the encoder. We conducted comprehensive evaluations of DSGNet on three typical surface defect datasets: Neu‐Seg, MSD and MT. While maintaining an extremely low complexity with only 0.49 M parameters, DSGNet achieved impressive mIoU scores of 83.39%, 91.61% and 80.72% on three datasets, respectively. These results indicate that DSGNet is a promising solution that balances lightweight design and detection accuracy for industrial real‐time detection systems, demonstrating strong potential for practical deployment. Our code is available at https://github.com/young‐zyy/DSGNet .

Magnetic ResonanceImaging (MRI) is widely used in examining and diagnosing prostate diseases due to its high resolution. However, the diverse morphology of prostate tissue presents a significant challenge for precise gland segmentation. Convolutional Neural Networks have demonstrated effectiveness in segmenting prostate regions. Nevertheless, their limited capability in extracting global long-range semantic features often leads to unstable network segmentation performance. To address these challenges, we propose a Deep Transformer-based Vnet framework (DT-VNet), which consists of a symmetric encoder-decoder architecture that explores global contextual features and retains local feature information. To effectively learn global and local features, We propose the Deep Union Transformer (DU-Trans) as an encoding base module for capturing comprehensive information. Additionally, we introduce a Pool Fusion Attention (PFA) module for decoding, which emphasizes learning context dependencies and interaction relationships. PFA can also facilitate the fusion of deep and shallow features. To our knowledge, this is the first study about deep transformer-based Vnet framework for prostate segmentation. We validate and compare our method on several public datasets against current state-of-the-art methods. The results demonstrate the superior performance of our proposed method in segmenting 3D prostate MRI.

The high-quality assembly of large-scale aircraft components (LACs) is crucial for modern aircraft manufacturing. The spherical joints are important parts connecting the numerical control locators (NCLs) and LAC. Nevertheless, determining the position of spherical joint centers (SJCs) is challenging because more than half of the ball heads are wrapped inside the ball sockets. Furthermore, the multiaxis asynchronous motion of NCLs will cause internal forces during the posture adjustment process, reducing assembly quality. Hence, an online adaptive scheme with lower internal forces is proposed to obtain the high-precision SJC positions. First, an innovative mathematical model for determining SJC positions with a Jacobian matrix is constructed, and a high-precision solution is achieved via the adaptive estimation Jacobian matrix method, transforming problems that cannot be directly measured into computable solutions. Second, a multiaxis synchronization error is introduced and its mapping relationship with posture-coupled error is established to ensure coordinated NCL motion and reduce internal forces. Finally, the effectiveness of the proposed method is demonstrated through experiments. The maximum error for SJC positions is 0.19 mm, a notable improvement from 0.4 mm. By introducing synchronization error, the internal force is decreased by at least 54%, and its fluctuation range by 54.9%, thereby ensuring assembly quality. This study provides a new method for improving aircraft assembly quality.

Visible-infrared person re-identification (VI-ReID) task is to retrieve the same pedestrian across the visible and infrared modalities. The existing transformer-based works are constrained by the inherent structure of the ViT that feature collapse in deeper layers and the over-globalization of extracted features, resulting in incomplete learning of local and low-level features. However, these features are instrumental in representing and identifying elements within visible-infrared images more comprehensively, which increases the accuracy and robustness of cross-modal pedestrian matching. To solve the above problem, we propose the Local-Aware Residual Attention Vision Transformer (LAReViT) to enhance the learning of fine-grained local and shallow-level information to reinforce the feature discrimination and comprehensiveness in ViT. Specifically, the Local-Aware Residual (LAR) Module, which uses a novel Local Residual Attention (LRA) mechanism, is proposed to increase the fine-grained local information contained in feature extraction. In order to exploit fine-grained local information lost in lower-level visual features, the LRA in the LAR module adopts novel attention residual connections. Additionally, we propose a Positional Channel Reconstruction (PCR) Module that takes advantage of the local receptive field benefits of convolution. PCR reweights features within patches at the channel level, further facilitating the network emphasis on effective fine-grained local information. Finally, the novel Center Aggregation Loss (CAL) is designed to reduce modality discrepancies moderately and promote comprehensive feature extraction. Extensive experiments conducted on the SYSU-MM01, RegDB, and LLCM datasets demonstrate the state-of-the-art performance achieved by our proposed method. The code is available at https://github.com/Hua-XC/LAReViT .

Multi-spectral object Re-identification (ReID) aims to retrieve specific objects by leveraging complementary information from different image spectra. It delivers great advantages over traditional single-spectral ReID in complex visual environment. However, the significant distribution gap among different image spectra poses great challenges for effective multi-spectral feature representations. In addition, most of current Transformer-based ReID methods only utilize the global feature of class tokens to achieve the holistic retrieval, ignoring the local discriminative ones. To address the above issues, we step further to utilize all the tokens of Transformers and propose a cyclic token permutation framework for multi-spectral object ReID, dubbled TOP-ReID. More specifically, we first deploy a multi-stream deep network based on vision Transformers to preserve distinct information from different image spectra. Then, we propose a Token Permutation Module (TPM) for cyclic multi-spectral feature aggregation. It not only facilitates the spatial feature alignment across different image spectra, but also allows the class token of each spectrum to perceive the local details of other spectra. Meanwhile, we propose a Complementary Reconstruction Module (CRM), which introduces dense token-level reconstruction constraints to reduce the distribution gap across different image spectra. With the above modules, our proposed framework can generate more discriminative multi-spectral features for robust object ReID. Extensive experiments on three ReID benchmarks (i.e., RGBNT201, RGBNT100 and MSVR310) verify the effectiveness of our methods. The code is available at https://github.com/924973292/TOP-ReID.

In the rapidly expanding logistics sector, parcel singulation has emerged as a significant bottleneck. To address this, we propose an automated parcel singulator utilizing a sparse actuator array, which presents an optimal balance between cost and efficiency, albeit requiring a sophisticated control policy. In this study, we frame the parcel singulation issue as a Markov Decision Process with a variable state space dimension, addressed through a deep reinforcement learning (RL) algorithm complemented by a State Space Standardization Module (S3). Distinct from previous RL approaches, our methodology initially considers the non-stationary environment during the problem modeling phase. To counter this challenge, the S3 module standardizes the dynamic input state, thereby stabilizing the RL training process. We validate our method through simulation experiments in complex environments, comparing it with several baseline algorithms. Results indicate that our algorithm excels in parcel singulation tasks, achieving a higher success rate and enhanced efficiency.

Causal confusion, characterized by the learning of spurious correlations, detrimentally affects the generalization and effectiveness of reinforcement learning (RL) algorithms, especially in environments without latent confounders often encountered in robot autonomous navigation tasks. This study addresses this gap by developing a causal structure within a Partially Observable Markov Decision Process (POMDP). Subsequently, we introduce a targeted intervention that mitigates the influence of spurious correlations by isolating causally significant state variables and discarding irrelevant inputs. Testing in three real-world scenarios confirms the approach’s feasibility and superiority in enhancing the RL algorithms’ performance and generalization ability, signifying a promising step towards more robust online RL frameworks.

In many brain network studies, brain functional connectivity data is extracted from neuroimaging data and then used for disease prediction. For now, brain disease data not only has a small sample but also has the problem of high dimensional and nonlinear. Therefore, deep clustering on brain functional connectivity data is very challenging. To solve these problems, we propose a Soft-orthogonal Constrained Dual-stream Encoder with Self-supervised clustering network (SSCDE), which consists of a pretext task and downstream task, which can fully mine the effective information in brain disease data. In the pretext task, we use two brain disease data under the same category to do cross-domain learning to obtain effective information from the same dataset. In the downstream task, to reduce redundancy and avoid negative coding, we propose a soft-orthogonal constrained dual-stream encoder to encode features separately. At the same time, we use the pseudo labels given by the pretext task as prior information for self-supervised learning. We conduct validation on different brain disease recognition tasks, and the result have proved that the proposed framework has achieved good performance compared with the unsupervised clustering analysis algorithms. To our knowledge, this is the first cross-domain assisted recognition study on brain functional connectivity data. The code is available at https://github.com/hulu88/SSCDE .

Abstract Deep graph clustering is an unsupervised learning task that divides nodes in a graph into disjoint regions with the help of graph auto-encoders. Currently, such methods have several problems, as follows. (1) The deep graph clustering method does not effectively utilize the generated pseudo-labels, resulting in sub-optimal model training results. (2) Each cluster has a different confidence level, which affects the reliability of the pseudo-label. To address these problems, we propose a Deep Self-supervised Attribute Graph Clustering model (DSAGC) to fully leverage the information of the data itself. We divide the proposed model into two parts: an upstream model and a downstream model. In the upstream model, we use the pseudo-label information generated by spectral clustering to form a new high-confidence distribution with which to optimize the model for a higher performance. We also propose a new reliable sample selection mechanism to obtain more reliable samples for downstream tasks. In the downstream model, we only use the reliable samples and the pseudo-label for the semi-supervised classification task without the true label. We compare the proposed method with 17 related methods on four publicly available citation network datasets, and the proposed method generally outperforms most existing methods in three performance metrics. By conducting a large number of ablative experiments, we validate the effectiveness of the proposed method.

Visible-Infrared Person Re-Identification (VI-ReID) is a challenging retrieval task under complex modality changes. Existing methods usually focus on extracting discriminative visual features while ignoring the reliability and commonality of visual features between different modalities. In this paper, we propose a novel deep learning framework named Progressive Modality-shared Transformer (PMT) for effective VI-ReID. To reduce the negative effect of modality gaps, we first take the gray-scale images as an auxiliary modality and propose a progressive learning strategy. Then, we propose a Modality-Shared Enhancement Loss (MSEL) to guide the model to explore more reliable identity information from modality-shared features. Finally, to cope with the problem of large intra-class differences and small inter-class differences, we propose a Discriminative Center Loss (DCL) combined with the MSEL to further improve the discrimination of reliable features. Extensive experiments on SYSU-MM01 and RegDB datasets show that our proposed framework performs better than most state-of-the-art methods. For model reproduction, we release the source code at https://github.com/hulu88/PMT.

Facial Expression Recognition (FER) in the wild poses significant challenges due to realistic occlusions, illumination, scale, and head pose variations of the facial images. In this article, we propose an Edge-AI-driven framework for FER. On the algorithms aspect, we propose two attention modules, Arbitrary-oriented Spatial Pooling (ASP) and Scalable Frequency Pooling (SFP), for effective feature extraction to improve classification accuracy. On the systems aspect, we propose an edge-cloud joint inference architecture for FER to achieve low-latency inference, consisting of a lightweight backbone network running on the edge device, and two optional attention modules partially offloaded to the cloud. Performance evaluation demonstrates that our approach achieves a good balance between classification accuracy and inference latency.

Colonic adenocarcinoma is a disease severely endangering human life caused by mucosal epidermal carcinogenesis. The segmentation of potentially cancerous glands is the key in the detection and diagnosis of colonic adenocarcinoma. The appearance of cancerous tissue is different in gland segmentation in colon pathological images, and it is impossible to accurately segment the changes of glands from benign to malignant using a single network. Given these issues, a two-path gland segmentation algorithm of colon pathological image based on local semantic guidance is proposed in this paper. The improved candidate region search algorithm is adopted to expand the original image data set and generate sub-datasets sensitive to specific features. Then, the semantic feature-guided model is employed to extract the local adenocarcinoma features and acts on the backbone network together with context feature extraction based on the attention mechanism. In this way, a larger receptive field and more local feature information are obtained, the learning ability of the network to the morphological features of glands is enhanced, and the performance of automatic gland segmentation is finally improved. The algorithm is verified on Warwick Qu-Dataset. Compared with the current popular segmentation algorithms, our algorithm has good performance in Dice coefficient, F1 score, and Hausdorff distance on different types of test sets.

Deep learning methods have shown significant performance in medical image analysis tasks. However, they generally act like ”black box” without explanations in both feature extraction and decision processes, leading to lack of clinical insights and high risk assessments. To aid deep learning in envisioning diseases with visual clues, we propose Representation Group-Disentangling Network (RGD-Net), which can completely disentangle feature space of input X-ray images into several independent feature groups, each corresponding to a specific disease. Taking several semantically related and labeled X-ray images as input, RGD-Net firstly extracts completely group-disentangled representations of diseases through Group-Disentangle Module, which applies group-swap and linking operations to construct latent space by enforcing semantic consistency of attributes. To prevent learning degenerate representations defined as shortcut problem, we further introduce adversarial constricts on mapping from features to diseases, thus avoiding model collapse with former free-form disentanglement. Experiments on chestxray-14 and ChestXpert datasets demonstrate that RGD-Net are effective in predicting diseases with remarkable advantages, which leverage potential factors contributing to different diseases, thus enhancing interpretability in working patterns of deep learning methods.

Video-based person re-identification aims to associate the video clips of the same person across multiple non-overlapping cameras. Spatial-temporal representations can provide richer and complementary information between frames, which are crucial to distinguish the target person when occlusion occurs. This paper proposes a novel Pyramid Spatial-Temporal Aggregation (PSTA) framework to aggregate the frame-level features progressively and fuse the hierarchical temporal features into a final video-level representation. Thus, short-term and long-term temporal information could be well exploited by different hierarchies. Furthermore, a Spatial-Temporal Aggregation Module (STAM) is proposed to enhance the aggregation capability of PSTA. It mainly consists of two novel attention blocks: Spatial Reference Attention (SRA) and Temporal Reference Attention (TRA). SRA explores the spatial correlations within a frame to determine the attention weight of each location. While TRA extends SRA with the correlations between adjacent frames, temporal consistency information can be fully explored to suppress the interference features and strengthen the discriminative ones. Extensive experiments on several challenging benchmarks demonstrate the effectiveness of the proposed PSTA, and our full model reaches 91.5% and 98.3% Rank-1 accuracy on MARS and DukeMTMC-VID benchmarks. The source code is available at https://github.com/WangYQ9/VideoReID-PSTA.

Segmentation methods only work for a single imaging modality usually suffer from the low spatial resolution in positron emission tomography (PET) or low contrast in computed tomography (CT) when the tumor region is inhomogeneous or not obvious. To address this problem, we develop a segmentation method combining the advantages and disadvantages of PET and CT. Firstly, the initial contours are obtained by the presegmentation of PET images using region growing and mathematical morphology. The initial contours can be used to automatically obtain the seed points required for random walk on PET and CT images, at the same time, they can be also used as a constraint in the random walk on CT images to solve the shortcoming that the tumor areas are not obvious if the CT images have not been enhanced. For the reason that CT provides essential details on anatomic structures, the anatomic structures of CT can be used to improve the weight of random walk on PET images. Finally, the similarity matrices obtained by random walk on PET and CT images are weighted to obtain identical results on PET and CT images. Our methods achieve an average DSC of 0.8456 ± 0.0703 on 14 patients with lung cancer. Our method has much better performance when the tumors are inhomogeneous on PET images and not obvious on CT images.

Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-the-art algorithms, there is a lack of video sequences captured in the wild rather than in constrained laboratory environment. In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment. In particular, for each object, we shoot seven videos involving various challenging factors, namely scale change, rotation, perspective distortion, motion blur, occlusion, out-of-view, and unconstrained. The ground truth is carefully annotated semi-manually to ensure the quality. Moreover, eleven state-of-the-art algorithms are evaluated on the benchmark using two evaluation metrics, with detailed analysis provided for the evaluation results. We expect the proposed benchmark to benefit future studies on planar object tracking.

Existing studies have shown that neuronal functional networks (NFNs) exhibit small-world properties. However, the issue of whether NFNs have any other complex network topology properties remains unresolved. In this paper, we introduced a new hierarchical clustering-based method that can clearly indicate the hierarchical modular organization of NFNs. Based on the modularity function Q proposed by Newman, we can divide the NFNs into suitable sub-modules. We proposed a new measure function to calculate the correlations between pairs of spike trains without requiring binning of the spike trains through small time windows. This method can be used to analyze the level of synchronization between spike trains and functional connectivity relationships between neurons. We analyzed NFNs constructed from multi-electrode recordings in rat brain cerebral cortexes in vivo. These rats had been trained to perform different working memory cognitive tasks. The results show that NFNs exhibit a clear hierarchical modular organization in rat brains. These results provided evidence confirming that the brain networks are complex. This can also be used as a means of studying the relationship between neuronal functional organization and cognitive behavioral tasks.

Graph theory is a very useful tool in the study of functional and anatomical network in the brain. It had been widely used in the functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) signals. Only very few studies analyzed the neuronal connections composed of individually recorded neurons. Particularly applying in the case of neuronal functional networks of animal behavior-dependent was rare. Scientists have found the small-world network properties in the functional network derived from fMRI and EEG signals. Whether there are existing small-world properties in the neuronal networks of multi-electrode recording? We use graph theory techniques to construct and analyze the neuronal networks. In the functional networks of a simultaneously recorded population of neurons in prefrontal cortex of the rat, in a Y-maze working memory task, we find that the neuronal connection density is highly relevant to rat behavior. We find there is a small-world effect in the neuronal functional network compared to a random graph with the same size and average connection density. We also find that small-world properties have a great relationship to correlation coefficient threshold selection. These findings indicate that neuronal functional networks of multi-electrode recordings are also small-world networks. Network connection topology and connection density are related to the working memory tasks in the rat.