Author name cluster

Jin Ding

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

YNIMG Journal 2026 Journal Article

VSSI-TBM: A variational sparse source imaging method based on time basis matrix

Tianyu Gao
Jin Ding
Wen Li
Fulong Wang
Yujie Ma
Ruonan Wang
Yang Gao
Xiaolin Ning

Source imaging algorithms have been widely used to localize functional and lesion areas. Brain source reconstruction is limited by complex experimental environments (noise interference, distributed brain activity, acquisition systems, etc.), and range estimation is not accurate. This study proposes a variational sparse source imaging method based on the time basis matrix (VSSI-TBM) algorithm. VSSI-TBM permits the source spatial signal to consist of several temporal basis functions by using low-rank decomposition to extract effective signals. In a compressed space, mixed-norm constraints and a cortical source variation operator ensure spatial sparsity and smoothness. In clinical examinations or research, other a priori information regarding brain activity may be available. VSSI-TBM using lead field guide constraints can further enhance the reconstruction results. The simulation results demonstrate the robust performance of VSSI-TBM in environments with a low signal-to-noise ratio (SNR), large sources ( > 11 cm 2 ), and multiple sources. Additionally, integrating prior information enhances the imaging performance in complex environments. The algorithm is evaluated using an open-source dataset and an optically pumped magnetometer-based magnetoencephalography (OPM-MEG) system with a noisy 30-channel uniform layout. The results reveal a strong robustness of the spatial range reconstruction. Moreover, the combination of prior information effectively improves the imaging performance of the OPM-MEG system.

Details DOI

JBHI Journal 2025 Journal Article

Frequency-Aware B-Line and Pleural Line Analysis in Lung Ultrasound Videos

Kaihui Yang
Guangyu Guo
Ying Zhang
Linxuan Pang
Zhaohui Zheng
Ruyu Liu
Jin Ding
Dingwen Zhang

Accurately identifying B-lines and pleural line (P-line) in lung ultrasound (LUS) videos is valuable for evaluating certain lung conditions. However, manual interpretation remains subjective and highly dependent on operator expertise. Existing deep learning methods often suffer from performance degradation due to speckle noise and motion artifacts. Moreover, the limited availability of LUS video data annotated for multiple diagnostic features such as B-lines and the P-line limits model development. Therefore, this paper introduces ILD-LUS, a new clinical LUS database designed based on interstitial lung disease (ILD) analysis by category labeling, comprising 2, 149 ultrasound videos (193, 410 frames). Also, we construct an external test set based on the public Covid-BLUES dataset for the evaluation of B-lines and P-line recognition in different pulmonary pathologies. Then, we propose a novel video analysis framework that integrates wavelet enhancement with temporal attention modeling. Specifically, we employ a dual-component frequency feature enhancement method using the Discrete Wavelet Transform (DWT), which effectively suppresses noise while preserving important landmarks. Subsequently, an adaptive attention module is introduced to model long-range temporal dependencies and improve dynamic feature representation across consecutive frames. Experimental results show that the proposed method achieves over 94% AUC and 82% ACC for both B-lines and P-line classification on both the ILD-LUS and Covid-BLUES datasets, outperforming existing methods. These findings demonstrate the robustness and generalizability of our approach across different pathological conditions. Overall, the proposed framework shows strong potential for supporting clinical decision-making in LUS analysis. The code is available at https://github.com/KaIi-github/WaveLUS.

Details DOI

JBHI Journal 2025 Journal Article

MHKD: Multi-Step Hybrid Knowledge Distillation for Low-Resolution Whole Slide Images Glomerulus Detection

Xiangsen Zhang
Longfei Han
Chenchu Xu
Zhaohui Zheng
Jin Ding
Xianghui Fu
Dingwen Zhang
Junwei Han

Glomerulus detection is a critical component of renal histopathology assessment, essential for diagnosing glomerulonephritis. To mitigate the increasing workload on pathologists, AI-assisted diagnostic methods based on high-resolution digital pathology whole slide images have been developed. However, these current AI-assisted approaches are limited to high-resolution whole slide images, necessitating expensive digital scanner equipment, high image storage costs, and significant computational complexity. To address this limitation, this paper pioneers a method for facilitating glomerulus detection in low-resolution human kidney pathology images. Specifically, we propose a novel multi-step hybrid knowledge distillation method. Our method distills both the global features and the semantic information through a hybrid knowledge distillation strategy that integrates offline and online knowledge distillation, where the information from high-resolution pathological images is successively transferred to student model from the global features in the shallow network layers to the semantic information of the back-end through a multi-step training strategy. Experimental results on two datasets show that the proposed method achieves effective detection outcomes for low-resolution kidney pathology images. Compared to other state-of-the-art detection techniques, our method achieves an ${AP}_{0. 5: 0. 95}$ improvement of 23. 1% on the private LN dataset and 15. 9% on the public HUBMAP dataset.

Details DOI

YNIMG Journal 2025 Journal Article

Noise and artifact suppression in SQUID and wearable OPM-MEG: A systematic review of background, physiological, and Technical interference

Ruonan Wang
Yujie Ma
Ruochen Zhao
Jin Ding
Ling Li
Yanfei Yang
Fulong Wang
Zhiqiang Cao

Magnetoencephalography (MEG) is a non-invasive imaging technique that captures neural activity with high spatio-temporal resolution. In recent years, novel wearable devices based on Optically Pumped Magnetometer (OPM) have emerged as a new driving force for advancing MEG due to their cost-effectiveness, portability, and mobility. In practical applications, MEG signals are frequently influenced by various interference sources, resulting in degradation of signal quality. Consequently, numerous suppression techniques have been proposed to overcome these challenges. This manuscript presents a comprehensive review of the most advanced methods for suppressing MEG noise or artifacts, with a specific focus on mitigating background noise, physiological artifacts (such as those caused by heartbeat, eye movements, and muscle contractions), as well as technical artifacts (including system-related artifacts associated with devices, motion-induced artifacts, and metal-induced artifacts). Additionally, the current limitations and challenges of these approaches in real-world scenarios are highlighted. Reviewing nearly a decade of research, there is an urgent need for a lightweight noise analysis framework in the complex measurement environment of wearable OPM-MEG devices. This framework should be capable of effectively detecting, classifying, and suppressing individual and combined MEG interference. By addressing this need, we can enhance the reliability and practicality of MEG signals while advancing brain science research.

Details DOI

EAAI Journal 2025 Journal Article

Two-layer knowledge graph transformer network-based question and answer explainable recommendation

Ying Li
Ming Li
Jin Ding
Yixue Bai

The question and answer (Q&A) recommendation in community question answering (CQA) helps users quickly and accurately find the desired Q&A. However, existing studies face the problems of sparse interaction data, cold starts, and a lack of explanations. This paper proposes a novel Q&A explainable recommendation approach based on a two-layer knowledge graph transformer network. It alleviates the sparse data and cold start problem by the novel two-layer knowledge graph. First, a two-layer knowledge graph in CQA is constructed. The interaction layer helps to enrich the associations between users and questions and answers (Q&As). The semantic layer provides semantic associations and reflects contextual domain knowledge. Second, a critical meta-path recognition module is constructed to learn the critical meta-paths between users and documents from the interaction layer. Then, a user and Q&A embedding method based on a two-layer knowledge graph is proposed to enhance the user and Q&A representations. Finally, a recommendation and explanation layer is established to obtain personalized Q&A recommendation results and corresponding explanations. Compared with the baselines, the proposed method shows superior performance. It achieves average improvements of 21. 28%, 28. 41% and 27. 18% in precision, recall and F1-measure, respectively, in the top- K Q&A recommendation separately. It improves the area under the curve and F1-measure of the click-through rate prediction recommendation by 11. 32% and 23. 06%, respectively.

Details DOI

AIIM Journal 2023 Journal Article

Fair and equitable AI in biomedical research and healthcare: Social science perspectives

Renate Baumgartner
Payal Arora
Corinna Bath
Darja Burljaev
Kinga Ciereszko
Bart Custers
Jin Ding
Waltraud Ernst

Artificial intelligence (AI) offers opportunities but also challenges for biomedical research and healthcare. This position paper shares the results of the international conference “Fair medicine and AI” (online 3–5 March 2021). Scholars from science and technology studies (STS), gender studies, and ethics of science and technology formulated opportunities, challenges, and research and development desiderata for AI in healthcare. AI systems and solutions, which are being rapidly developed and applied, may have undesirable and unintended consequences including the risk of perpetuating health inequalities for marginalized groups. Socially robust development and implications of AI in healthcare require urgent investigation. There is a particular dearth of studies in human-AI interaction and how this may best be configured to dependably deliver safe, effective and equitable healthcare. To address these challenges, we need to establish diverse and interdisciplinary teams equipped to develop and apply medical AI in a fair, accountable and transparent manner. We formulate the importance of including social science perspectives in the development of intersectionally beneficent and equitable AI for biomedical research and healthcare, in part by strengthening AI health evaluation.

Details DOI