Search Results - "Ye, Xujiong"

Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images by Ye, Xujiong, Lin, Xinyu, Dehmeshki, Jamshid, Slabaugh, Greg, Beddoe, Gareth

“…In this paper, a new computer tomography (CT) lung nodule computer-aided detection (CAD) method is proposed for detecting both solid nodules and ground-glass…”
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Segmentation of Pulmonary Nodules in Thoracic CT Scans: A Region Growing Approach by Dehmeshki, J., Amin, H., Valdivieso, M., Xujiong Ye

“…This paper presents an efficient algorithm for segmenting different types of pulmonary nodules including high and low contrast nodules, nodules with…”
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Automatic Individual Pig Detection and Tracking in Pig Farms by Zhang, Lei, Gray, Helen, Ye, Xujiong, Collins, Lisa, Allinson, Nigel

“…Individual pig detection and tracking is an important requirement in many video-based pig monitoring applications. However, it still remains a challenging task…”
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MDA-Unet: A Multi-Scale Dilated Attention U-Net for Medical Image Segmentation by Amer, Alyaa, Lambrou, Tryphon, Ye, Xujiong

“…The advanced development of deep learning methods has recently made significant improvements in medical image segmentation. Encoder–decoder networks, such as…”
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Rethinking the transfer learning for FCN based polyp segmentation in colonoscopy by Wen, Yan, Zhang, Lei, Meng, Xiangli, Ye, Xujiong

“…Besides the complex nature of colonoscopy frames with intrinsic frame formation artefacts such as light reflections and the diversity of polyp types/shapes,…”
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ResDUnet: A Deep Learning-Based Left Ventricle Segmentation Method for Echocardiography by Amer, Alyaa, Ye, Xujiong, Janan, Faraz

“…Segmentation of echocardiographic images is an essential step for assessing the cardiac functionality, as indicative clinical measures can be obtained from the…”
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Esophageal Abnormality Detection Using DenseNet Based Faster R-CNN With Gabor Features by Ghatwary, Noha, Ye, Xujiong, Zolgharni, Massoud

“…Early detection of esophageal abnormalities can help in preventing the progression of the disease into later stages. During esophagus examination,…”
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DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction by Guang Yang, Simiao Yu, Hao Dong, Slabaugh, Greg, Dragotti, Pier Luigi, Xujiong Ye, Fangde Liu, Arridge, Simon, Keegan, Jennifer, Yike Guo, Firmin, David

“…Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not…”
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MCA-Unet: A multiscale context aggregation U-Net for the segmentation of COVID-19 lesions from CT images by Amer, Alyaa, Ye, Xujiong

“…The pandemic of coronavirus disease (COVID-19) caused the world to face an existential health crisis. COVID-19 lesions segmentation from CT images is nowadays…”
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Dual-Stream Spatiotemporal Networks with Feature Sharing for Monitoring Animals in the Home Cage by Nwokedi, Ezechukwu Israel, Bains, Rasneer Sonia, Bidaut, Luc, Ye, Xujiong, Wells, Sara, Brown, James M

“…This paper presents a spatiotemporal deep learning approach for mouse behavioral classification in the home-cage. Using a series of dual-stream architectures…”
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Tissue-type mapping of gliomas by Raschke, Felix, Barrick, Thomas R., Jones, Timothy L., Yang, Guang, Ye, Xujiong, Howe, Franklyn A.

“…To develop a statistical method of combining multimodal MRI (mMRI) of adult glial brain tumours to generate tissue heterogeneity maps that indicate tumour…”
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Weakly supervised pre-training for brain tumor segmentation using principal axis measurements of tumor burden by Mckone, Joshua E., Lambrou, Tryphon, Ye, Xujiong, Brown, James M.

“…Introduction State-of-the-art multi-modal brain tumor segmentation methods often rely on large quantities of manually annotated data to produce acceptable…”
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Early esophageal adenocarcinoma detection using deep learning methods by Ghatwary, Noha, Zolgharni, Massoud, Ye, Xujiong

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A novel unified deep neural networks methodology for use by date recognition in retail food package image by Gong, Liyun, Thota, Mamatha, Yu, Miao, Duan, Wenting, Swainson, Mark, Ye, Xujiong, Kollias, Stefanos

“…There exist various types of information on retail food packages, including use by date, food product name and so on. The correct coding of use by dates on…”
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An optimisation-based iterative approach for speckle tracking echocardiography by Azarmehr, Neda, Ye, Xujiong, Howes, Joseph D., Docking, Benjamin, Howard, James P., Francis, Darrel P., Zolgharni, Massoud

“…Speckle tracking is the most prominent technique used to estimate the regional movement of the heart based on echocardiograms. In this study, we propose an…”
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A marker-less human motion analysis system for motion-based biomarker identification and quantification in knee disorders by Armstrong, Kai, Zhang, Lei, Wen, Yan, Willmott, Alexander P, Lee, Paul, Ye, Xujiong

“…In recent years the healthcare industry has had increased difficulty seeing all low-risk patients, including but not limited to suspected osteoarthritis (OA)…”
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Automatic Graph Cut Segmentation of Lesions in CT Using Mean Shift Superpixels by Ye, Xujiong, Beddoe, Gareth, Slabaugh, Greg

“…This paper presents a new, automatic method of accurately extracting lesions from CT data. It first determines, at each voxel, a five-dimensional (5D) feature…”
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Automated detection of lung nodules in CT images using shape-based genetic algorithm by Dehmeshki, Jamshid, Ye, Xujiong, Lin, XinYu, Valdivieso, Manlio, Amin, Hamdan

“…Abstract A shape-based genetic algorithm template-matching (GATM) method is proposed for the detection of nodules with spherical elements. A spherical-oriented…”
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Volumetric Quantification of Atherosclerotic Plaque in CT Considering Partial Volume Effect by Dehmeshki, J., Xujiong Ye, Amin, H., Abaei, M., XinYu Lin, Qanadli, S.D.

“…Coronary artery calcification (CAC) is quantified based on a computed tomography (CT) scan image. A calcified region is identified. Modified expectation…”
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NEPHROBLASTOMA ANALYSIS IN MRI IMAGES by Kaba, Djibril, McFarlane, Nigel, Dong, Feng, Graf, Norbert, Ye, Xujiong

“…The annotation of the tumour from medical scans is a crucial step in nephroblastoma treatment. Therefore, an accurate and reliable segmentation method is…”
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