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• Journal article
  Zhang C, Wu Y, Boyer-Chammard J, Jewell S, Strong AJ, Yang G, Boutelle MGet al., 2026,

  Multi-Scale Signal-Image Fusion Model Based On ECoGfor Automatic Detection of Early-stage Traumatic Brain Injury.

  , IEEE Trans Biomed Eng, Vol: PP

  Spreading depolarizations (SDs) are key drivers of secondary brain injury, yet existing bedside monitoring methods that use electrocorticography (ECoG) analyze electrodes and frequency bands separately, thereby obscuring the joint spatiotemporal patterns of SDs. Therefore, this paper introduces a multi-scale signal-image fusion framework that for the first time enables SDmonitoring as a joint multi-modal multi-band spectral image-based analysis. The ECoG signal is converted into a persistent spectral de-weighted spectrogram (PSd-Spec) and joined with multi-band features, through Transformer-CNN jointly empowered blocks: Multi-Channel and Band Transformer Block (MCBTB) and Multi-Scale Adaptive Fusion (MSAF). The network extracts short- and long-range dynamics in a multi-scale time window, while an attention-driven channel weighting module adaptively models the spatial propagation of the electrode strips. On 500h of neuro-ICU recordings, the proposed approach achieved 92.6% accuracy, 84.9% sensitivity. Relative to the best single-modality base line, performance improved by at least 18%, and SD onset was identified on average of 8 min before expert observation. The results suggest that multi-scale fusion of spectral images with ECoG signals yields a clinically actionable early-warning approach and extends quantitative imaging methods to intracranial electrophysiology.

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• Journal article
  Cheng CW, Huang J, Zhang Y, Yang G, Schönlieb CB, Aviles-Rivero AIet al., 2026,

  Mamba neural operator: Who wins? transformers vs. state-space models for PDEs

  , Journal of Computational Physics, Vol: 548, ISSN: 0021-9991

  Partial differential equations (PDEs) are widely used to model complex physical systems, but solving them efficiently remains a significant challenge. Recently, Transformers have emerged as the preferred architecture for PDEs due to their ability to capture intricate dependencies. However, they struggle with representing continuous dynamics and long-range interactions. To overcome these limitations, we introduce the Mamba Neural Operator (MNO), a novel framework that enhances neural operator-based techniques for solving PDEs. MNO establishes a formal theoretical connection between structured state-space models (SSMs) and neural operators, offering a unified structure that can adapt to diverse architectures, including Transformer-based models. By leveraging the structured design of SSMs, MNO captures long-range dependencies and continuous dynamics more effectively than traditional Transformers. Through extensive analysis, we show that MNO significantly boosts the expressive power and accuracy of neural operators, making it not just a complement but a superior framework for PDE-related tasks, bridging the gap between efficient representation and accurate solution approximation.

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  • Citations: 4
• Journal article
  Jing P, Lee K, Zhang Z, Zhou H, Yuan Z, Gao Z, Zhu L, Papanastasiou G, Fang Y, Yang Get al., 2026,

  Reason like a radiologist: Chain-of-thought and reinforcement learning for verifiable report

  , MEDICAL IMAGE ANALYSIS, Vol: 109, ISSN: 1361-8415
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• Journal article
  Ma X, Tao Y, Zhang Z, Zhang Y, Wang X, Zhang S, Ji Z, Zhang Y, Chen Q, Yang Get al., 2026,

  Test-time generative augmentation for medical image segmentation

  , MEDICAL IMAGE ANALYSIS, Vol: 109, ISSN: 1361-8415
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• Journal article
  Li K, Xiao X, Zhong Z, Yang Get al., 2026,

  Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning

  , IEEE Open Journal of Engineering in Medicine and Biology, Vol: 7, Pages: 86-93, ISSN: 2644-1276

  Goal: Protein-ligand binding complexes are ubiquitous and essential to life. Protein-ligand binding affinity prediction (PLA) quantifies the binding strength between ligands and proteins, providing crucial insights for discovering and designing potential candidate ligands. While recent advances have been made in predicting protein-ligand complex structures, existing algorithms for interaction and affinity prediction suffer from a sharp decline in performance when handling ligands bound with novel unseen proteins. Methods: We propose IPBind, a geometric deep learning-based computational method, enabling robust predictions by leveraging interatomic potential between complex's bound and unbound status. Results: Experimental results on widely used binding affinity prediction benchmarks demonstrate the effectiveness and universality of IPBind. Meanwhile, it provids atom-level insights into prediction. Conclusions: This work highlight the advantage of leveraging machine learning interatomic potential for predicting protein-ligand binding affinity.

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• Journal article
  Zhang S, Nan Y, Fang Y, Wang S, Liu Y, Papanastasiou G, Gao Z, Li S, Walsh S, Yang Get al., 2026,

  Dynamical multi-order responses and global semantic-infused adversarial learning: A robust airway segmentation method

  , MEDICAL IMAGE ANALYSIS, Vol: 108, ISSN: 1361-8415
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• Journal article
  Ma J, Jiang M, Fang X, Chen J, Wang Y, Yang Get al., 2026,

  Hybrid aggregation strategy with double inverted residual blocks for lightweight salient object detection

  , NEURAL NETWORKS, Vol: 194, ISSN: 0893-6080
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• Journal article
  Khalique Z, Scott AD, Ferreira PF, Molto M, Nielles-Vallespin S, Pennell DJet al., 2026,

  Diffusion Tensor CMR Assessment of the Microstructural Response to Dobutamine Stress in Health and Comparison With Patients With Recovered Dilated Cardiomyopathy.

  , Circ Cardiovasc Imaging, Vol: 19

  BACKGROUND: Contractile reserve assessment assesses myocardial performance and prognosis. The microstructural mechanisms that facilitate increased cardiac function have not been described, but can be studied using diffusion tensor cardiovascular magnetic resonance. Resting microstructural contractile function is characterized by reorientation of aggregated cardiomyocytes (sheetlets) from wall-parallel in diastole to a more wall-perpendicular configuration in systole, with the diffusion tensor cardiovascular magnetic resonance parameter E2A defining their orientation, and sheetlet mobility defining the angle through which they rotate. We used diffusion tensor cardiovascular magnetic resonance to identify the microstructural response to dobutamine stress in healthy volunteers and then compared with patients with recovered dilated cardiomyopathy (rDCM). METHODS: In this first-of-its-kind prospective observational study, 20 healthy volunteers and 32 patients with rDCM underwent diffusion tensor cardiovascular magnetic resonance at rest, during dobutamine, and on recovery. RESULTS: In healthy volunteers, both diastolic and systolic E2A increased with dobutamine stress (13±3° to 17±5°; P<0.001 and 59±11° to 65±7°; P=0.002). Sheetlet mobility remained unchanged (45±11° to 49±10°; P=0.19), but biphasic mean E2A increased (36±6° to 41±4°; P<0.001). In rDCM, diastolic E2A at rest was higher than in healthy volunteers (20±8° versus 13±3°, P<0.001), and sheetlet mobility was reduced (34±12° versus 45±11°; P<0.001). During dobutamine stress, rDCM diastolic and systolic E2A increased compared with rest (20±8° to 24±10°; P=0.001 and 54±13° to 63±11°; P=0.005). However, sheetlet mobility in patients with rDCM failed to increase with dobutamine to healthy levels (39±13° versus 49±

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• Journal article
  Jameel A, Smith J, Akgun S, Bain P, Nandi D, Jones B, Quest R, Gedroyc W, Yousif Net al., 2026,

  Creation and clinical utility of a 3D atlas-based model for visualising brain nuclei targeted by MR-guided focused ultrasound thalamotomy for tremor.

  , Biomed Phys Eng Express, Vol: 12

  Magnetic resonance guided focused ultrasound (MRgFUS) thalamotomy is an established treatment for tremor. MRgFUS utilises ultrasound to non-invasively thermally ablate or 'lesion' tremorgenic tissue. The success of treatment is contingent on accurate lesioning as assessed by tremor improvement and minimisation of adverse effects. However, coordinate planning and post-procedure lesion visualisation are difficult as the key targets, cannot be seen on standard clinical imaging. Thus, a computational tool is needed to aid target visualisation. A 3D atlas-based model was created using the Schaltenbrand-Wahren atlas. Key nuclei were manually delineated, interpolated and smoothed in 3D Slicer to create the model. Evaluation of targeting approaches across a seven-year period and patient-specific analyses of tremor treatments were performed. The anatomical position of MRgFUS lesions in the model were compared against varying clinical outcomes. The model provides an anatomical visualisation of how the change in targeting approach led to improved tremor suppression and a reduction in adverse effects for patients. This study demonstrates the successful development of a 3D atlas-based computational model of the brain target nuclei in MRgFUS thalamotomy and its clinical utility for tremor treatment analysis.

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• Conference paper
  Zhang Z, Lee K, Jing P, Deng W, Zhou H, Jin Z, Huang J, Gao Z, Marshall DC, Fang Y, Yang Get al., 2026,

  GEMA-Score: Granular Explainable Multi-Agent Scoring Framework for Radiology Report Evaluation

  , Pages: 13025-13033, ISSN: 2159-5399

  Automatic medical report generation has the potential to support clinical diagnosis, reduce the workload of radiologists, and demonstrate potential for enhancing diagnostic consistency. However, current evaluation metrics often fail to reflect the clinical reliability of generated reports. Overlap-based methods overlook fine-grained details (e.g., location, sever-ity), diagnostic metrics are constrained by fixed vocabularies. Some diagnostic metrics are limited by fixed vocabularies or templates, reducing their ability to capture diverse clinical expressions. LLM-based metrics lack interpretable reasoning, limiting trust in clinical settings. Therefore, we propose a Granular Explainable Multi-Agent Score (GEMA-Score) in this paper, which conducts both objective quantification and subjective evaluation through a large language model-based multi-agent workflow. Our GEMA-Score parses structured reports and employs stable calculations through interactive exchanges of information among agents to assess disease diagnosis, location, severity, and uncertainty. Additionally, an LLM-based scoring agent evaluates completeness, readability, and clinical terminology while providing explanatory feedback. Extensive experiments show that GEMA-Score achieves the highest correlation with human experts on public datasets (Kendall = 0.69 on ReXVal; 0.45 on RadEvalX), demonstrating improved clinical scoring reliability.

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