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• Journal article
  Nardi C, Magnini A, Rastrelli V, Zantonelli G, Calistri L, Lorini C, Luzzi V, Gori L, Ciani L, Morecchiato F, Simonetti V, Peired AJ, Landini N, Cavigli E, Yang G, Guiot J, Tomassetti S, Colagrande Set al., 2024,

  Laboratory data and broncho-alveolar lavage on Covid-19 patients with no intensive care unit admission: Correlation with chest CT features and clinical outcomes.

  , Medicine (Baltimore), Vol: 103

  Broncho-alveolar lavage (BAL) is indicated in cases of uncertain diagnosis but high suspicion of Sars-Cov-2 infection allowing to collect material for microbiological culture to define the presence of coinfection or super-infection. This prospective study investigated the correlation between chest computed tomography (CT) findings, Covid-19 Reporting and Data System score, and clinical outcomes in Coronavirus disease 2019 (Covid-19) patients who underwent BAL with the aim of predicting outcomes such as lung coinfection, respiratory failure, and hospitalization length based on chest CT abnormalities. Study population included 34 patients (range 38-90 years old; 20 males, 14 females) with a positive nucleic acid amplification test for Covid-19 infection, suitable BAL examination, and good quality chest CT scan in the absence of lung cancer history. Pulmonary coinfections were found in 20.6% of patients, predominantly caused by bacteria. Specific correlations were found between right middle lobe involvement and pulmonary co-infections. Severe lung injury (PaO2/FiO2 ratio of 100-200) was associated with substantial involvement of right middle, right upper, and left lower lobes. No significant correlation was found between chest CT findings and inflammatory markers (C-reactive protein, procalcitonin) or hospitalization length of stay. Specific chest CT patterns, especially in right middle lobe, could serve as indicators for the presence of co-infections and disease severity in noncritically ill Covid-19 patients, aiding clinicians in timely interventions and personalized treatment strategies.

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• Journal article
  Kang J, Yang G, Wang Y, V Wang J, Wang Q, Zhu Get al., 2024,

  Study of aging mechanisms in LiFePO4 4 batteries with various SOC levels using the zero-sum pulse method

  , ISCIENCE, Vol: 27
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• Journal article
  Yang G, Edwards B, Bakas S, Dou Q, Xu D, Li X, Wang Wet al., 2024,

  Federated learning as a catalyst for digital healthcare innovations

  , PATTERNS, Vol: 5, ISSN: 2666-3899
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  • Citations: 2
• Journal article
  Li H, Nan Y, Del Ser J, Yang Get al., 2024,

  Large-kernel attention for 3D medical image segmentation

  , Cognitive Computation, Vol: 16, Pages: 2063-2077, ISSN: 1866-9956

  Automated segmentation of multiple organs and tumors from 3D medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep learning methods can aid in diagnosing and treating cancer. However, organs often overlap and are complexly connected, characterized by extensive anatomical variation and low contrast. In addition, the diversity of tumor shape, location, and appearance, coupled with the dominance of background voxels, makes accurate 3D medical image segmentation difficult. In this paper, a novel 3D large-kernel (LK) attention module is proposed to address these problems to achieve accurate multi-organ segmentation and tumor segmentation. The advantages of biologically inspired self-attention and convolution are combined in the proposed LK attention module, including local contextual information, long-range dependencies, and channel adaptation. The module also decomposes the LK convolution to optimize the computational cost and can be easily incorporated into CNNs such as U-Net. Comprehensive ablation experiments demonstrated the feasibility of convolutional decomposition and explored the most efficient and effective network design. Among them, the best Mid-type 3D LK attention-based U-Net network was evaluated on CT-ORG and BraTS 2020 datasets, achieving state-of-the-art segmentation performance when compared to avant-garde CNN and Transformer-based methods for medical image segmentation. The performance improvement due to the proposed 3D LK attention module was statistically validated.

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• Journal article
  Mo Y, Liu F, Yang G, Wang S, Zheng J, Wu F, Papiez BW, Mcilwraith D, He T, Guo Yet al., 2024,

  Labelling with dynamics: A data-efficient learning paradigm for medical image segmentation

  , MEDICAL IMAGE ANALYSIS, Vol: 95, ISSN: 1361-8415
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• Journal article
  Gao J, Xu Z, Guo B, Lei Y, Yang Get al., 2024,

  Longitudinal Ultrasonic Vibration-Assisted Planing Method for Processing Micro-Pyramid Arrays

  , MICROMACHINES, Vol: 15
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• Conference paper
  Hu J, Wu P, Li Q, Wang S, Xiao X, Niu Z, Wang B, Yang Get al., 2024,

  A Smart Strategy for Photoresponsive Molecules: Utilizing Generative Pre-trained Transformer and TDDFT Calculations in Drug Delivery.

  , Pages: 1-7

  Photoresponsive drug delivery stands as a pivotal frontier in smart drug administration, leveraging the non-invasive, stable, and finely tunable nature of light-triggered methodologies. The Generative Pre-trained Transformer (GPT) has been employed for generating molecular structures. In our study, we harnessed GPT-2 on the QM7b dataset to refine a UV-GPT model with adapters, enabling the generation of molecules responsive to UV light excitation. Utilizing the Coulomb matrix as a molecular descriptor, we predicted the excitation wavelengths of these molecules. Furthermore, we validated the excited state properties through Quantum chemical simulations. The synergy of these findings underscores the successful application of GPT technology in this critical domain.

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• Journal article
  Xie C, Ye J, Ma X, Dong L, Zhao G, Cheng J, Yang G, Lai Xet al., 2024,

  Automated Segmentation of Brain Gliomas in Multimodal MRI Data

  , INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Vol: 34, ISSN: 0899-9457
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• Journal article
  Liu Y, Li Y, Jiang M, Wang S, Ye S, Walsh S, Yang Get al., 2024,

  SOCR-YOLO : Small Objects Detection Algorithm in Medical Images

  , INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Vol: 34, ISSN: 0899-9457
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• Journal article
  Zhong W, Zhang H, Gao Z, Hau WK, Yang G, Liu X, Xu Let al., 2024,

  Distraction-aware hierarchical learning for vascular structure segmentation in intravascular ultrasound images

  , COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, Vol: 115, ISSN: 0895-6111
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