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  • Journal article
    Yang M, Xiao X, Liu Z, Sun L, Guo W, Cui L, Sun D, Zhang P, Yang Get al., 2020,

    Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification

    , SCIENTIFIC PROGRAMMING, Vol: 2020, ISSN: 1058-9244
  • Journal article
    Hu S, Gao Y, Niu Z, Jiang Y, Li L, Xiao X, Wang M, Fang EF, Menpes-Smith W, Xia J, Ye H, Yang Get al., 2020,

    Weakly supervised deep learning for COVID-19 infection detection and classification from CT images

    , IEEE Access, Vol: 8, Pages: 118869-18883, ISSN: 2169-3536

    An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.

  • Journal article
    Ali A-R, Li J, Kanwal S, Yang G, Hussain A, O'Shea Jet al., 2020,

    A Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic Images

    , FRONTIERS IN MEDICINE, Vol: 7
  • Journal article
    Medjeral-Thomas NR, Lawrence C, Condon M, Sood B, Warwicker P, Brown H, Pattison J, Bhandari S, Barratt J, Turner N, Cook HT, Levy JB, Lightstone L, Pusey C, Galliford J, Cairns TD, Griffith Met al., 2020,

    Randomized, controlled trial of tacrolimus and prednisolone monotherapy for adults with de novo minimal change disease: a multicenter, randomized, controlled trial (vol 15, pg 209, 2020)

    , Clinical Journal of the American Society of Nephrology, Vol: 15, Pages: 1027-1027, ISSN: 1555-9041
  • Journal article
    Ali A-R, Li J, Yang G, O'Shea SJet al., 2020,

    A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

    , PEERJ COMPUTER SCIENCE, ISSN: 2376-5992
  • Journal article
    Prendecki M, Clarke C, Cairns T, Cook T, Roufosse C, Thomas D, Willicombe M, Pusey CD, McAdoo SPet al., 2020,

    Anti-glomerular basement membrane disease during the COVID-19 pandemic

    , Kidney International, ISSN: 0085-2538
  • Journal article
    McAdoo S, Prendecki M, Tanna A, Bhatt T, Bhangal G, McDaid J, masuda E, Cook H, Tam F, Pusey Cet al., 2020,

    Spleen tyrosine kinase inhibition is an effective treatment for established vasculitis in a pre-clinical model

    , Kidney International, Vol: 97, Pages: 1196-1207, ISSN: 0085-2538

    The anti-neutrophil cytoplasm antibody (ANCA)-associated vasculitides (AAV) are a group of life-threatening multi-system diseases characterized by necrotising inflammation of small blood vessels and crescentic glomerulonephritis. ANCA are thought to play a direct pathogenic role. Previous studies have shown that spleen tyrosine kinase (SYK) is phosphorylated during ANCA-induced neutrophil activation in vitro. However, the role of SKY in vivo is unknown. Here, we studied its role in the pathogenesis of experimental autoimmune vasculitis, a pre-clinical model of myeloperoxidase-ANCA-induced pauci-immune systemic vasculitis in the Wistar Kyoto rat. Up-regulation of SYK expression in inflamed renal and pulmonary tissue during early autoimmune vasculitis was confirmed by immunohistochemical and transcript analysis. R406, the active metabolite of fostamatinib, a small molecule kinase inhibitor with high selectivity for SYK, inhibited ANCA-induced pro-inflammatory responses in rat leucocytes in vitro. In an in vivo study, treatment with fostamatinib for 14 days after disease onset resulted in rapid resolution of urinary abnormalities, significantly improved renal and pulmonary pathology, and preserved renal function. Short-term exposure to fostamatinib did not significantly affect circulating myeloperoxidase-ANCA levels, suggesting inhibition of ANCA-induced inflammatory mechanisms in vivo. Finally, SYK expression was demonstrated within inflammatory glomerular lesions in ANCA-associated glomerulonephritis in patients, particularly within CD68+ve monocytes/macrophages. Thus, our data indicate that SYK inhibition warrants clinical investigation in the treatment of AAV.

  • Journal article
    Tarkin JM, Wall C, Gopalan D, Aloj L, Manavaki R, Fryer TD, Aboagye EO, Bennett MR, Peters JE, Rudd JHF, Mason JCet al., 2020,

    Novel approach to imaging active takayasu arteritis using somatostatin receptor positron emission tomography/magnetic resonance imaging.

    , Circulation: Cardiovascular Imaging, Vol: 13, Pages: 1-3, ISSN: 1941-9651

    Although 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) is an important diagnostic test for Takayasu arteritis (TAK),118F-FDG lacks inflammatory cell selectivity and cannot accurately distinguish arteritis from metabolically active vascular remodeling.2 This observation has led to the search for more sensitive and specific PET tracers for TAK. Macrophage activation antigen SST2 (somatostatin receptor subtype-2) PET represents a potential alternative imaging biomarker for defining disease activity in TAK, as macrophages are a major feature of the inflammatory infiltrate. We aimed to determine the ability of SST2 PET/magnetic resonance imaging (MRI) to detect arteritis in 2 patients with clinically active TAK.

  • Journal article
    Yang G, Chen J, Gao Z, Li S, Ni H, Angelini E, Wong T, Mohiaddin R, Nyktari E, Wage R, Xu L, Zhang Y, Du X, Zhang H, Firmin D, Keegan Jet al., 2020,

    Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention

    , Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications, Vol: 107, Pages: 215-228, ISSN: 0167-739X

    Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (0.27 s to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60–68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.

  • Journal article
    Li M, Wang C, Zhang H, Yang Get al., 2020,

    MV-RAN: Multiview recurrent aggregation network for echocardiographic sequences segmentation and full cardiac cycle analysis

    , Computers in Biology and Medicine, Vol: 120, ISSN: 0010-4825

    Multiview based learning has generally returned dividends in performance because additional information can be extracted for the representation of the diversity of different views. The advantage of multiview based learning fits the purpose of segmenting cardiac anatomy from multiview echocardiography, which is a non-invasive, low-cost and low-risk imaging modality. Nevertheless, it is still challenging because of limited training data, a poor signal-to-noise ratio of the echocardiographic data, and large variances across views for a joint learning. In addition, for a better interpretation of pathophysiological processes, clinical decision-making and prognosis, such cardiac anatomy segmentation and quantitative analysis of various clinical indices should ideally be performed for the data covering the full cardiac cycle. To tackle these challenges, a multiview recurrent aggregation network (MV-RAN) has been developed for the echocardiographic sequences segmentation with the full cardiac cycle analysis. Experiments have been carried out on multicentre and multi-scanner clinical studies consisting of spatio-temporal (2D + t) datasets. Compared to other state-of-the-art deep learning based methods, the MV-RAN method has achieved significantly superior results (0.92 ± 0.04 Dice scores) for the segmentation of the left ventricle on the independent testing datasets. For the estimation of clinical indices, our MV-RAN method has also demonstrated great promise and will undoubtedly propel forward the understanding of pathophysiological processes, computer-aided diagnosis and personalised prognosis using echocardiography.

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