Generative Adversarial Networks Category
Multipath cycleGAN for harmonization of paired and unpaired low-dose lung computed tomography reconstruction kernels
Dec. 5, 2025—Aravind R Krishnan, Thomas Z Li, Lucas W Remedios, Michael E Kim, Chenyu Gao, Gaurav Rudravaram, Elyssa M McMaster, Adam M Saunders, Shunxing Bao, Kaiwen Xu, Lianrui Zuo, Kim L Sandler, Fabien Maldonado, Yuankai Huo, Bennett A Landman, Medical Physics, Volume 52, Issue 11, DOI: https://doi.org/10.1002/mp.70120 Abstract Background Reconstruction kernels in computed tomography (CT) introduce...
Anatomy-Guided Multi-Path CycleGAN for Lung CT Kernel Harmonization
Dec. 5, 2025—Aravind Krishnan, Thomas Li, Lucas Walker Remedios, Kaiwen Xu, Lianrui Zuo, Kim L Sandler, Fabien Maldonado, Bennett Allan Landman, MIDL 2025, Link to paper: https://openreview.net/pdf?id=w3p7GddsQ8 Abstract Accurate quantitative measurement in lung computed tomography (CT) imaging often re- lies on consistent kernel reconstruction across scanners and manufacturers. Harmonization can reduce measurement variability caused by heterogeneous...
Investigating the impact of kernel harmonization and deformable registration on inspiratory and expiratory chest CT images for people with COPD
Dec. 5, 2025—Aravind R Krishnan, Yihao Liu, Kaiwen Xu, Michael E Kim, Lucas W Remedios, Gaurav Rudravaram, Adam M Saunders, Bradley W Richmond, Kim L Sandler, Fabien Maldonado, Bennett A Landman, Lianrui Zuo, Medical Imaging 2025: Clinical and Biomedical Imaging, Volume 13410, 531-541 , DOI: https://doi.org/10.1117/12.3048827 Abstract Paired inspiratory-expiratory Computed Tomography (CT) scans enable quantification of gas...
Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks
Apr. 24, 2024—Aravind R. Krishnan, Kaiwen Xu, Thomas Li, Lucas W. Remedios, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman. “Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks.”Med Phys. 2024;1-14.https://doi.org/10.1002/mp.17028 Abstract Background The kernel used in CT image reconstruction is an important factor that determines the texture of the CT image. Consistency of reconstruction...
Nucleus subtype classification using inter-modality learning
Dec. 19, 2023—Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios, Ho Hin Lee, Leon Y. Cai, Thomas Li, Ruining Deng, Can Cui, Jia Li, Qi Liu, Ken S. Lau, Joseph T. Roland, Mary K. Washington, Lori A. Coburn, Keith T. Wilson, Yuankai Huo, Bennett A. Landman (2024). Nucleus subtype classification using inter-modality learning. SPIE Medical Imaging 2024 :...
Inter-vendor harmonization of CT reconstruction kernels using unpaired image translation
Dec. 1, 2023—Aravind R. Krishnan, Kaiwen Xu, Thomas Li, Chenyu Gao, Lucas W. Remedios, Praitayini Kanakaraj, Ho Hin Lee, Shunxing Bao, Kim L. Sandler, Fabien Maldonado, Ivana Išgum, and Bennett A. Landman “Inter-vendor harmonization of CT reconstruction kernels using unpaired image translation”, Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129261D (2 April 2024); https://doi.org/10.1117/12.3006608 Abstract The reconstruction kernel in computed tomography (CT) generation determines the texture of the image. Consistency...
Inpainting Missing Tissue in Multiplexed Immunofluorescence Imaging
Dec. 10, 2021—Shunxing Bao, Yucheng Tang, Ho Hin Lee, Riqiang Gao, Qi Yang, Xin Yu, Sophie Chiron, Lori A. Coburn, Keith T. Wilson, Joseph T. Roland, Bennett A. Landman, Yuankai Huo. “Inpainting Missing Tissue in Multiplexed Immunofluorescence Imaging” Medical Imaging 2022: Digital and Computational Pathology. International Society for Optics and Photonics, accepted Full text: [TBD] Abstract Multiplex immunofluorescence (MxIF) is...
Learning Implicit Brain MRI Manifolds with Deep Learning
Dec. 22, 2017—Bermudez, C., Plassard, A.J., Davis, T.L., Newton, A.T., Resnick, S.M., and Landman, B.A. (2017) “Learning implicit brain MRI manifolds with deep learning.” arXiv preprint arXiv:1801.01847 Full Text: https://arxiv.org/pdf/1801.01847.pdf Abstract An important task in image processing and neuroimaging is to extract quantitative information from the acquired images in order to make observations about the presence of disease...