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• Journal article
  Lyu J, Qin C, Wang S, Wang F, Li Y, Wang Z, Guo K, Ouyang C, Taenzer M, Liu M, Sun L, Sun M, Li Q, Shi Z, Hua S, Li H, Chen Z, Zhang Z, Xin B, Metaxas DN, Yiasemis G, Teuwen J, Zhang L, Chen W, Zhao Y, Tao Q, Pang Y, Liu X, Razumov A, V Dylov D, Dou Q, Yan K, Xue Y, Du Y, Dietlmeier J, Garcia-Cabrera C, Hemidi ZA-H, Vogt N, Xu Z, Zhang Y, Chu Y-H, Chen W, Bai W, Zhuang X, Qin J, Wu L, Yang G, Qu X, Wang H, Wang Cet al., 2025,

  The state-of-the-art in cardiac MRI reconstruction: Results of the CMRxRecon challenge in MICCAI 2023

  , MEDICAL IMAGE ANALYSIS, Vol: 101, ISSN: 1361-8415
  • Cite
  • Citations: 3
• Journal article
  Inglese M, Boccato T, Ferrante M, Islam S, Williams M, Waldman AD, O'Neill K, Aboagye EO, Toschi Net al., 2025,

  Genotype Characterization in Primary Brain Gliomas via Unsupervised Clustering of Dynamic PET Imaging of Short-Chain Fatty Acid Metabolism

  , IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES, Vol: 9, Pages: 460-467, ISSN: 2469-7311
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• Journal article
  Li M, Xu P, Hu J, Tang Z, Yang Get al., 2025,

  From challenges and pitfalls to recommendations and opportunities: Implementing federated learning in healthcare

  , MEDICAL IMAGE ANALYSIS, Vol: 101, ISSN: 1361-8415
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  • Citations: 9
• Journal article
  Wang F, Luo Y, Munoz C, Wen K, Luo Y, Huang J, Wu Y, Khalique Z, Molto M, Rajakulasingam R, de Silva R, Pennell DJ, Ferreira PF, Scott AD, Nielles-Vallespin S, Yang Get al., 2025,

  Enhanced DTCMR With Cascaded Alignment and Adaptive Diffusion

  , IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 44, Pages: 1866-1877, ISSN: 0278-0062
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• Conference paper
  Vinciguerra G, Yang G, Gatterbauer W, Dunne Cet al., 2025,

  Reproducibility Report for ACM SIGMOD 2024 Paper: “On The Reasonable Effectiveness of Relational Diagrams”

  , Pages: 60-63, ISSN: 0730-8078

  The paper [1] introduces Relational Diagrams, a visual representation of relational queries, and presents two experimental studies: a textbook analysis and a user study. The authors made all necessary files and scripts available for reproducibility through an Open Science Framework project. During the reproducibility process, a minor error was identified in the analysis of the textbook queries, though it does not affect the main conclusions of the paper. The figures reproduced for the user study are nearly identical to the ones presented in the paper. Overall, all major findings of the paper were successfully reproduced.

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• Journal article
  Jin W, Wang N, Tao T, Jiang M, Xing Y, Zhao B, Wu H, Duan H, Yang Get al., 2025,

  A prompting multi-task learning-based veracity dissemination consistency reasoning augmentation for few-shot fake news detection

  , ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, Vol: 144, ISSN: 0952-1976
  • Cite
  • Citations: 3
• Journal article
  Wang C, Jiang M, Li Y, Wei B, Li Y, Wang P, Yang Get al., 2025,

  MP-FocalUNet: Multiscale parallel focal self-attention U-Net for medical image segmentation

  , COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, Vol: 260, ISSN: 0169-2607
  • Cite
  • Citations: 1
• Journal article
  Wang J, Ruan D, Li Y, Wang Z, Wu Y, Tan T, Yang G, Jiang Met al., 2025,

  Data augmentation strategies for semi-supervised medical image segmentation

  , PATTERN RECOGNITION, Vol: 159, ISSN: 0031-3203
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• Journal article
  Wang Z, Wang F, Qin C, Lyu J, Ouyang C, Wang S, Li Y, Yu M, Zhang H, Guo K, Shi Z, Li Q, Xu Z, Zhang Y, Li H, Hua S, Chen B, Sun L, Sun M, Li Q, Chu Y-H, Bai W, Qin J, Zhuang X, Prieto C, Young A, Markl M, Wang H, Wu L-M, Yang G, Qu X, Wang Cet al., 2025,

  CMRxRecon2024: A Multimodality, Multiview k-Space Dataset UniversalMachine for Accelerated Cardiac MRI

  , RADIOLOGY-ARTIFICIAL INTELLIGENCE, Vol: 7, ISSN: 2638-6100
  • Cite
  • Citations: 4
• Journal article
  Hasan MK, Zhu H, Yang G, Yap CHet al., 2025,

  Deep learning image registration for cardiac motion estimation in adult and fetal echocardiography via a focus on anatomic plausibility and texture quality of warped image.

  , Comput Biol Med, Vol: 187

  Temporal echocardiography image registration is important for cardiac motion estimation, myocardial strain assessments, and stroke volume quantifications. Deep learning image registration (DLIR) is a promising way to achieve consistent and accurate registration results with low computational time. DLIR seeks the image deformation that enables the moving image to be warped to match the fixed image. We propose that, during DLIR training, a greater focus on the warped moving image's anatomic plausibility and image texture can support robust results, and we show that it has sufficient robustness to be applied to both fetal and adult echocardiography. Our proposed framework includes (1) an anatomic shape-encoded constraint to preserve physiological myocardial and left ventricular anatomical topologies in the warped image, (2) a data-driven texture constraint to preserve good texture features in the warped image, and (3) a multi-scale training algorithm to improve accuracy. Our experiments demonstrate a strong correlation between the shape-encoded constraint and good anatomical topology and between the data-driven texture constraint and image textures. They improve different aspects of registration results in a non-overlapping way. We demonstrate that these methods can successfully register both fetal and adult echocardiography using our multi-demographic fetal dataset and the public CAMUS adult dataset, despite the inherent differences between adult and fetal echocardiography. Our approach also outperforms traditional non-DL gold standard registration approaches, including optical flow and Elastix, and could be translated into more accurate and precise clinical quantification of cardiac ejection fraction, demonstrating potential for clinical utility.

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