arXiv subjects

Weimin Zhong

Publications and source records attributed to Weimin Zhong.

8 recordsLinked to original sources

arXiv2026

AT-Attn: Temporal-Aware Cross-Attention for Longitudinal Multimodal Alzheimer's Disease Diagnosis

In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits. Integrating these multimodal observations may improve diagnostic assessment, but naive fusion can degrade performance when MRI is noisy or intermittently unavailable. We propose AT-Attn, a temporal-aware multimodal framework that combines Change-and-Time encoding, time-biased asymmetric cross-attention, and gated fusion to integrate MRI with longitudinal clinical information. We evaluate AT-Attn on an MRI-retained ADNI cohort of 1,520 patients using structural MRI, six cognitive-scale trajectories, and seven static clinical variables under patient-level five-fold cross-validation. The main asymmetric AT-Attn model achieves accuracy 0.719+/-0.024, macro F1 0.721+/-0.023, ROC-AUC 0.873+/-0.013, and PR-AUC 0.783+/-0.018, outperforming unimodal and naive multimodal fusion baselines while remaining competitive with strong tabular baselines. These results suggest that a temporal-aware and constrained fusion strategy can help structural MRI contribute clinically relevant complementary information for patient-level AD diagnosis support.

cs.CV↗

arXiv2026

ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection

Deep learning-based industrial anomaly detectors often behave as black boxes, making it hard to justify decisions with physically meaningful defect evidence. We propose ZSG-IAD, a multimodal vision-language framework for zero-shot grounded industrial anomaly detection. Given RGB images, sensor images, and 3D point clouds, ZSG-IAD generates structured anomaly reports and pixel-level anomaly masks. ZSG-IAD introduces a language-guided two-hop grounding module: (1) anomaly-related sentences select evidence-like latent slots distilled from multimodal features, yielding coarse spatial support; (2) selected slots modulate feature maps via channel-spatial gating and a lightweight decoder to produce fine-grained masks. To improve reliability, we further apply Executable-Rule GRPO with verifiable rewards to promote structured outputs, anomaly-region consistency, and reasoning-conclusion coherence. Experiments across multiple industrial anomaly benchmarks show strong zero-shot performance and more transparent, physically grounded explanations than prior methods. We will release code and annotations to support future research on trustworthy industrial anomaly detection systems.

cs.CV↗

arXiv2025

Multi-scale Cascaded Foundation Model for Whole-body Organs-at-risk Segmentation

Accurate segmentation of organs-at-risk (OARs) is vital for safe and precise radiotherapy and surgery. Most existing studies segment only a limited set of organs or regions, lacking a systematic treatment of OARs segmentation. We present a Multi-scale Cascaded Fusion Network (MCFNet) that aggregates features across multiple scales and resolutions. MCFNet consists of a Sharp Extraction Backbone for the downsampling path and a Flexible Connection Backbone for skip-connection fusion, strengthening representation learning in both stages. This design improves boundary localization and preserves fine structures while maintaining computational efficiency, enabling reliable performance even on low-resolution inputs. Experiments on an NVIDIA A6000 GPU using 36,131 image-mask pairs from 671 patients across 10 datasets show consistent robustness and strong cross-dataset generalization. An adaptive loss-aggregation strategy further stabilizes optimization and yields additional gains in accuracy and training efficiency. Through extensive validation, MCFNet outperforms existing methods, excelling in organ segmentation and providing reliable image-guided support for computer-aided diagnosis. Our solution aims to improve the precision and safety of radiotherapy and surgery while supporting personalized treatment, advancing modern medical technology. The code has been made available on GitHub: https://github.com/Henry991115/MCFNet.

eess.IV↗

arXiv2025

MSD-KMamba: Bidirectional Spatial-Aware Multi-Modal 3D Brain Segmentation via Multi-scale Self-Distilled Fusion Strategy

Numerous CNN-Transformer hybrid models rely on high-complexity global attention mechanisms to capture long-range dependencies, which introduces non-linear computational complexity and leads to significant resource consumption. Although knowledge distillation and sparse attention mechanisms can improve efficiency, they often fall short of delivering the high segmentation accuracy necessary for complex tasks. Balancing model performance with computational efficiency remains a critical challenge. In this work, we propose a novel 3D multi-modal image segmentation framework, termed MSD-KMamba, which integrates bidirectional spatial perception with multi-scale self-distillation. The bidirectional spatial aware branch effectively captures long-range spatial context dependencies across brain regions, while also incorporating a powerful nonlinear feature extraction mechanism that further enhances the model's ability to learn complex and heterogeneous patterns. In addition, the proposed multi-scale self-distilled fusion strategy strengthens hierarchical feature representations and improves the transfer of semantic information at different resolution levels. By jointly leveraging the bidirectional spatial perception branch and the multi-scale self-distilled fusion strategy, our framework effectively mitigates the bottleneck of quadratic computational complexity in volumetric segmentation, while simultaneously addressing the limitation of insufficient global perception. Extensive experiments on multiple standard benchmark datasets demonstrate that MSD-KMamba consistently outperforms state-of-the-art methods in segmentation accuracy, robustness, and generalization, while maintaining high computational efficiency and favorable scalability. The source code of MSD-KMamba is publicly available at https://github.com/daimao-zhang/MSD-KMamba.

cs.CV↗

arXiv2025

HiPerformer: A High-Performance Global-Local Segmentation Model with Modular Hierarchical Fusion Strategy

Both local details and global context are crucial in medical image segmentation, and effectively integrating them is essential for achieving high accuracy. However, existing mainstream methods based on CNN-Transformer hybrid architectures typically employ simple feature fusion techniques such as serial stacking, endpoint concatenation, or pointwise addition, which struggle to address the inconsistencies between features and are prone to information conflict and loss. To address the aforementioned challenges, we innovatively propose HiPerformer. The encoder of HiPerformer employs a novel modular hierarchical architecture that dynamically fuses multi-source features in parallel, enabling layer-wise deep integration of heterogeneous information. The modular hierarchical design not only retains the independent modeling capability of each branch in the encoder, but also ensures sufficient information transfer between layers, effectively avoiding the degradation of features and information loss that come with traditional stacking methods. Furthermore, we design a Local-Global Feature Fusion (LGFF) module to achieve precise and efficient integration of local details and global semantic information, effectively alleviating the feature inconsistency problem and resulting in a more comprehensive feature representation. To further enhance multi-scale feature representation capabilities and suppress noise interference, we also propose a Progressive Pyramid Aggregation (PPA) module to replace traditional skip connections. Experiments on eleven public datasets demonstrate that the proposed method outperforms existing segmentation techniques, demonstrating higher segmentation accuracy and robustness. The code is available at https://github.com/xzphappy/HiPerformer.

cs.CV↗

arXiv2023

OpenGCD: Assisting Open World Recognition with Generalized Category Discovery

A desirable open world recognition (OWR) system requires performing three tasks: (1) Open set recognition (OSR), i.e., classifying the known (classes seen during training) and rejecting the unknown (unseen$/$novel classes) online; (2) Grouping and labeling these unknown as novel known classes; (3) Incremental learning (IL), i.e., continual learning these novel classes and retaining the memory of old classes. Ideally, all of these steps should be automated. However, existing methods mostly assume that the second task is completely done manually. To bridge this gap, we propose OpenGCD that combines three key ideas to solve the above problems sequentially: (a) We score the origin of instances (unknown or specifically known) based on the uncertainty of the classifier's prediction; (b) For the first time, we introduce generalized category discovery (GCD) techniques in OWR to assist humans in grouping unlabeled data; (c) For the smooth execution of IL and GCD, we retain an equal number of informative exemplars for each class with diversity as the goal. Moreover, we present a new performance evaluation metric for GCD called harmonic clustering accuracy. Experiments on two standard classification benchmarks and a challenging dataset demonstrate that OpenGCD not only offers excellent compatibility but also substantially outperforms other baselines. Code: https://github.com/Fulin-Gao/OpenGCD.

cs.CV↗

arXiv2021

Deep adaptive fuzzy clustering for evolutionary unsupervised representation learning

Cluster assignment of large and complex images is a crucial but challenging task in pattern recognition and computer vision. In this study, we explore the possibility of employing fuzzy clustering in a deep neural network framework. Thus, we present a novel evolutionary unsupervised learning representation model with iterative optimization. It implements the deep adaptive fuzzy clustering (DAFC) strategy that learns a convolutional neural network classifier from given only unlabeled data samples. DAFC consists of a deep feature quality-verifying model and a fuzzy clustering model, where deep feature representation learning loss function and embedded fuzzy clustering with the weighted adaptive entropy is implemented. We joint fuzzy clustering to the deep reconstruction model, in which fuzzy membership is utilized to represent a clear structure of deep cluster assignments and jointly optimize for the deep representation learning and clustering. Also, the joint model evaluates current clustering performance by inspecting whether the re-sampled data from estimated bottleneck space have consistent clustering properties to progressively improve the deep clustering model. Comprehensive experiments on a variety of datasets show that the proposed method obtains a substantially better performance for both reconstruction and clustering quality when compared to the other state-of-the-art deep clustering methods, as demonstrated with the in-depth analysis in the extensive experiments.

cs.CV↗

arXiv2021

Representation Evaluation Block-based Teacher-Student Network for the Industrial Quality-relevant Performance Modeling and Monitoring

Quality-relevant fault detection plays an important role in industrial processes, while the current quality-related fault detection methods based on neural networks main concentrate on process-relevant variables and ignore quality-relevant variables, which restrict the application of process monitoring. Therefore, in this paper, a fault detection scheme based on the improved teacher-student network is proposed for quality-relevant fault detection. In the traditional teacher-student network, as the features differences between the teacher network and the student network will cause performance degradation on the student network, representation evaluation block (REB) is proposed to quantify the features differences between the teacher and the student networks, and uncertainty modeling is used to add this difference in modeling process, which are beneficial to reduce the features differences and improve the performance of the student network. Accordingly, REB and uncertainty modeling is applied in the teacher-student network named as uncertainty modeling teacher-student uncertainty autoencoder (TSUAE). Then, the proposed TSUAE is applied to process monitoring, which can effectively detect faults in the process-relevant subspace and quality-relevant subspace simultaneously. The proposed TSUAE-based fault detection method is verified in two simulation experiments illustrating that it has satisfactory fault detection performance compared to other fault detection methods.

cs.LG↗