Publications
“A Deep Learning Approach to Horse Bone Segmentation from Digitally Reconstructed Radiographs”, in International Conference on Image Processing Theory, Tools, and Applications, 2019.
, “A deep learning approach to T1 mapping in quantitative MRI”, 36th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine & Biology (ESMRMB), Rotterdam, The Netherlands, vol. 32 (Suppl. 1). Magn Reson Mater Phy, 2019.
, “Deep learning based missing wedge artefact removal for electron tomography”, Microscopy Conference, Berlin, Germany. pp. 660-661, 2019.
, “Mixed-Scale Dense Convolutional Neural Network based Improvement of Glass Fiber-reinforced Composite CT Images”, 4th International Conference on Tomography of Materials & Structures. 2019.
, “BeadNet: a network for automated spherical marker detection in radiographs for geometry calibration”, in 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020, pp. 518-521. Download paper (2.16 MB)
, “CNN-based Deblurring of Terahertz Images”, in Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), 2020, vol. 4, pp. 323-330. Download paper (16.31 MB)
, “Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT”, in 10th Conference on Industrial Computed Tomography (ICT 2020), 2020.
, “Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets”, Science Direct Elsevier Neural Networks, vol. 121, pp. 101-121, 2020.
, “A low-cost geometry calibration procedure for a modular cone-beam X-ray CT system”, Nondestructive Testing and Evaluation , vol. 35, no. 3, pp. 252-265, 2020.
, “Ring Artifact Reduction in Sinogram Space Using Deep Learning”, in 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020. Download paper (2.49 MB)
, “Ringing Artefact Removal From Sparse View Tomosynthesis using Deep Neural Networks”, in The 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020, pp. 380-383. Download paper (570.79 KB)
, “Recurrent Inference Machines as inverse problem solvers for MR relaxometry”, Medical Image Analysis, vol. 74, pp. 1-11, 2021. Download paper (2.26 MB)
, “Recurrent Inference Machines as Inverse Problem Solvers for MR Relaxometry”, in MIDL 2021 - Medical Imaging with Deep Learning, 2021.
, “To Recurse or not to Recurse A Low Dose CT Study”, Progress in Artificial Intelligence, vol. 10, pp. 65–81, 2021.
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