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• Journal article
  Wu W, Long Y, Gao Z, Yang G, Cheng F, Zhang Jet al., 2025,

  Multi-Level Noise Sampling From Single Image for Low-Dose Tomography Reconstruction

  , IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 29, Pages: 1256-1268, ISSN: 2168-2194
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• Journal article
  Guiot J, Henket M, Gester F, André B, Ernst B, Frix A-N, Smeets D, Van Eyndhoven S, Antoniou K, Conemans L, Gote-Schniering J, Slabbynck H, Kreuter M, Sellares J, Tomos I, Yang G, Ribbens C, Louis R, Cottin V, Tomassetti S, Smith V, Walsh SLFet al., 2025,

  Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients.

  , Respir Res, Vol: 26

  BACKGROUND: Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapidly evolving interstitial lung disease (ILD), driving its mortality. Specific imaging-based biomarkers associated with the evolution of lung disease are needed to help predict and quantify ILD. METHODS: We evaluated the potential of an automated ILD quantification system (icolung®) from chest CT scans, to help in quantification and prediction of ILD progression in SSc-ILD. We used a retrospective cohort of 75 SSc-ILD patients to evaluate the potential of the AI-based quantification tool and to correlate image-based quantification with pulmonary function tests and their evolution over time. RESULTS: We evaluated a group of 75 patients suffering from SSc-ILD, either limited or diffuse, of whom 30 presented progressive pulmonary fibrosis (PPF). The patients presenting PPF exhibited more extensive lesions (in % of total lung volume (TLV)) based on image analysis than those without PPF: 3.93 (0.36-8.12)* vs. 0.59 (0.09-3.53) respectively, whereas pulmonary functional test showed a reduction in Force Vital Capacity (FVC)(pred%) in patients with PPF compared to the others : 77 ± 20% vs. 87 ± 19% (p < 0.05). Modifications of FVC and diffusing capacity of the lungs for carbon monoxide (DLCO) over time were correlated with longitudinal radiological ILD modifications (r=-0.40, p < 0.01; r=-0.40, p < 0.01 respectively). CONCLUSION: AI-based automatic quantification of lesions from chest-CT images in SSc-ILD is correlated with physiological parameters and can help in disease evaluation. Further clinical multicentric validation is necessary in order to confirm its potential in the prediction of patient's outcome and in treatment management.

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• Journal article
  Zhang S-Q, Niu Z, Anisimov A, Shi F, Deng S, Xiao X, Cao S-Q, Pan J-P, Wang H-L, Lagartos-Donate MJ, Bozbas NG, Wang P-J, Ai R, Li Y, Yang G, Lautrup S, Fang EFet al., 2025,

  NR1D1 Inhibition Enhances Autophagy and Mitophagy in Alzheimer's Disease Models

  , Aging and Disease, ISSN: 2152-5250
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• Journal article
  Xing X, Tang C, Murdoch S, Papanastasiou G, Guo Y, Xiao X, Cross-Zamirski J, Schonlieb C-B, Liang KX, Niu Z, Fang EF, Wang Y, Yang Get al., 2025,

  Artificial immunofluorescence in a flash: Rapid synthetic imaging from brightfield through residual diffusion

  , NEUROCOMPUTING, Vol: 612, ISSN: 0925-2312
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• Conference paper
  Dayarathna S, Islam KT, Zhuang B, Yang G, Cai J, Law M, Chen Zet al., 2025,

  McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis

  , Pages: 670-679

  Magnetic Resonance Imaging (MRI) is instrumental in clinical diagnosis, offering diverse contrasts that provide comprehensive diagnostic information. However, acquiring multiple MRI contrasts is often constrained by high costs, long scanning durations, and patient discomfort. Current synthesis methods, typically focused on single-image contrasts, fall short in capturing the collective nuances across various contrasts. Moreover, existing methods for multi-contrast MRI synthesis often fail to accurately map feature-level information across multiple imaging contrasts. We introduce McCaD (Multi-Contrast MRI Conditioned Adaptive Adversarial Diffusion), a novel framework leveraging an adversarial diffusion model conditioned on multiple contrasts for high-fidelity MRI synthesis. McCaD significantly enhances synthesis accuracy by employing a multi-scale, feature-guided mechanism, incorporating denoising and semantic encoders. An adaptive feature maximization strategy and a spatial feature-attentive loss have been introduced to capture more intrinsic features across multiple contrasts. This facilitates a precise and comprehensive feature-guided denoising process. Extensive experiments on tumor and healthy multi-contrast MRI datasets demonstrated that the McCaD outperforms state-of-the-art baselines quantitively and qualitatively. The code is available at https://github.com/sanuwanihewa/M.cCaD.

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  • Citations: 21
• Book chapter
  Jimenez-Pastor A, Cerdá-Alberich L, Kosvyra A, Nan Y, Xing X, Li R, Yang Get al., 2025,

  Data Harmonization and Challenges Toward the Generation of Repositories: Sharing Practices and Approaches

  , Trustworthy AI in Cancer Imaging Research, Pages: 121-142

  The harmonization of data is becoming increasingly important in imaging research. As the complexity and volume of data grow, integrating diverse datasets from different institutions, regions, and imaging modalities presents significant challenges and opportunities. This chapter delves into the role that data harmonization plays in overcoming these challenges and facilitating the generation of comprehensive data repositories that can be shared across research groups and institutions, detailing the different steps required to build such repositories including data de-identification, image annotation, quality control, and harmonization.

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  • Citations: 1
• Book chapter
  Xing X, Nan Y, Yang G, Papanastasiou G, Dìaz O, Osuala R, Lekadir K, Alberich LCet al., 2025,

  Generating Synthetic Data in Cancer Research

  , Trustworthy AI in Cancer Imaging Research, Pages: 81-101

  This chapter focuses on how deep learning-based generative algorithms are crucial for developing trustworthy AI datasets, especially in oncology. We start by outlining the basics of synthetic data generation in cancer research, introducing the concepts and techniques that enable the creation of realistic and diverse datasets. Following this, we highlight key successes and applications from the literature, showcasing the practical benefits and potential of these algorithms. We then evaluate the trustworthiness of synthetic data, discussing how to assess its quality and reliability for use in clinical and research settings. In the last section, we will identify challenges and opportunities that lie ahead, emphasizing the role of innovation in expanding the use and impact of synthetic data in cancer research.

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  • Citations: 2
• Journal article
  Huang J, Yang L, Wang F, Wu Y, Nan Y, Wu W, Wang C, Shi K, Aviles-Rivero AI, Schoenlieb C-B, Zhang D, Yang Get al., 2025,

  Enhancing global sensitivity and uncertainty quantification in medical image reconstruction with Monte Carlo arbitrary-masked mamba

  , MEDICAL IMAGE ANALYSIS, Vol: 99, ISSN: 1361-8415
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• Journal article
  Wang N, Jin W, Jing S, Bi H, Yang Get al., 2025,

  Learning with noisy labels via Mamba and entropy KNN framework

  , APPLIED SOFT COMPUTING, Vol: 169, ISSN: 1568-4946
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  • Citations: 2
• Journal article
  Gao K, Heinrich M, Yang G, Zhao H, Zhou X, Li Set al., 2025,

  Editorial: Traditional clinical symptoms and signs: how can they be used to investigate medications in the context of pharmacology?

  , Front Pharmacol, Vol: 16, ISSN: 1663-9812
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