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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Future Blog Post
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portfolio
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publications
An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder
Published in arXiv preprint, 2020
An algorithm for out-of-distribution attacks against neural network encoders.
Recommended citation: Liang, L., Ma, L., Qian, L., & Chen, J. (2020). "An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder." arXiv:2009.08016. https://arxiv.org/abs/2009.08016
CQ-VAE: Coordinate quantized VAE for uncertainty estimation with application to disk shape analysis from lumbar spine MRI images
Published in IEEE, 2021
CQ-VAE: a coordinate-quantized variational autoencoder for uncertainty estimation, applied to disk shape analysis from lumbar spine MRI.
Recommended citation: Qian, L., Chen, J., Urakov, T., Gu, W., & Liang, L. (2021). "CQ-VAE: Coordinate quantized VAE for uncertainty estimation with application to disk shape analysis from lumbar spine MRI images." IEEE. https://ieeexplore.ieee.org/abstract/document/9356321
Adversarial robustness study of convolutional neural network for lumbar disk shape reconstruction from MR images
Published in Proc. SPIE 11596, Medical Imaging 2021, 2021
A study of adversarial robustness for a CNN used in lumbar disk shape reconstruction from MR images.
Recommended citation: Chen, J., Qian, L., Urakov, T., Gu, W., & Liang, L. (2021). "Adversarial robustness study of convolutional neural network for lumbar disk shape reconstruction from MR images." Proc. SPIE 11596, Medical Imaging 2021. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11596/1159615/Adversarial-robustness-study-of-convolutional-neural-network-for-lumbar-disk/10.1117/12.2580852.short
A general approach to improve adversarial robustness of DNNs for medical image segmentation and detection
Published in SPIE Medical Imaging, 2021
A general approach for improving the adversarial robustness of deep neural networks used in medical image segmentation and detection.
Recommended citation: Ma, L., Chen, J., Qian, L., & Liang, L. (2021). "A general approach to improve adversarial robustness of DNNs for medical image segmentation and detection." SPIE Medical Imaging. https://spie.org/MI/conferencedetails/medical-image-processing
A sequential geometry-reconstruction-based deep learning approach to improve accuracy and consistence of lumbar spine MRI image segmentation
Published in Proc. SPIE 12926, Medical Imaging 2024, 2024
A sequential geometry-reconstruction-based deep learning approach for improving accuracy and consistency of lumbar spine MRI segmentation.
Recommended citation: Qian, L., Chen, J., Ma, L., Urakov, T., & Liang, L. (2024). "A sequential geometry-reconstruction-based deep learning approach to improve accuracy and consistence of lumbar spine MRI image segmentation." Proc. SPIE 12926, Medical Imaging 2024. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12926/1292634/A-sequential-geometry-reconstruction-based-deep-learning-approach-to-improve/10.1117/12.3007064.short
SymTC: A Symbiotic Transformer-CNN Net for Instance Segmentation of Lumbar Spine MRI
Published in Computers in Biology and Medicine, 2024
SymTC combines a Transformer and a CNN in a symbiotic architecture for instance segmentation of lumbar spine MRI.
Recommended citation: Chen, J., Qian, L., Ma, L., Urakov, T., Gu, W., & Liang, L. (2024). "SymTC: A Symbiotic Transformer-CNN Net for Instance Segmentation of Lumbar Spine MRI." Computers in Biology and Medicine. https://www.sciencedirect.com/science/article/abs/pii/S0010482524008801
A 3D Image Segmentation Study of U-Net on CT Images of the Human Aorta with Morphologically Diverse Anatomy
Published in bioRxiv preprint, 2024
A study of 3D U-Net segmentation on CT images of the human aorta across morphologically diverse anatomy.
Recommended citation: Chen, J., Qian, L., Wang, P., Sun, C., Qin, T., Kalyanasundaram, A., Zafar, M., Elefteriades, J., Sun, W., & Liang, L. (2024). "A 3D Image Segmentation Study of U-Net on CT Images of the Human Aorta with Morphologically Diverse Anatomy." bioRxiv. https://www.biorxiv.org/content/10.1101/2024.10.02.616348v2.abstract
A novel attention-based network for geometry reconstruction with error estimation from medical images
Published in Proc. SPIE 13406, Medical Imaging 2025, 2025
An attention-based network for geometry reconstruction from medical images with built-in error estimation.
Recommended citation: Qian, L., Chen, J., Ma, L., Urakov, T., Gu, W., & Liang, L. (2025). "A novel attention-based network for geometry reconstruction with error estimation from medical images." Proc. SPIE 13406, Medical Imaging 2025. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13406/3038529/A-novel-attention-based-network-for-geometry-reconstruction-with-error/10.1117/12.3038529.short
Attention-based Shape-Deformation networks for Artifact-Free geometry reconstruction of lumbar spine from MR images
Published in IEEE, 2025
An attention-based shape-deformation network for artifact-free geometry reconstruction of the lumbar spine from MR images.
Recommended citation: Qian, L., Chen, J., Ma, L., Urakov, T., Gu, W., & Liang, L. (2025). "Attention-based Shape-Deformation networks for Artifact-Free geometry reconstruction of lumbar spine from MR images." IEEE. https://ieeexplore.ieee.org/abstract/document/11080083
FEAorta: A Fully Automated Framework for Finite Element Analysis of the Aorta From 3D CT Images
Published in arXiv preprint, 2025
A fully automated deep-learning framework for patient-specific finite element analysis of the aorta directly from 3D CT images.
Recommended citation: Chen, J., Qian, L., Gong, R., Sun, C., Qin, T., Pham, T., Martin, C., Zafar, M., Elefteriades, J., Sun, W., & Liang, L. (2025). "FEAorta: A Fully Automated Framework for Finite Element Analysis of the Aorta From 3D CT Images." arXiv:2510.06621. https://arxiv.org/abs/2510.06621
A Computational Pipeline for Patient-Specific Modeling of Thoracic Aortic Aneurysm: From Medical Image to Finite Element Analysis
Published in Proc. SPIE 13929, Medical Imaging 2026: Clinical and Biomedical Imaging, 2026
A patient-specific computational pipeline that goes from medical image to finite element analysis for thoracic aortic aneurysm.
Recommended citation: Chen, J., Qian, L., Gong, R., Sun, C., Qin, T., Pham, T., Martin, C., Zafar, M., Elefteriades, J., Sun, W., & Liang, L. (2026). "A Computational Pipeline for Patient-Specific Modeling of Thoracic Aortic Aneurysm: From Medical Image to Finite Element Analysis." Proc. SPIE 13929, Medical Imaging 2026. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13929/139291B/A-computational-pipeline-for-patientspecific-modeling-of-thoracic-aortic-aneurysm/10.1117/12.3087786.full
Shape deformation networks for automated aortic valve finite element meshing from 3D CT images
Published in Proc. SPIE 13929, Medical Imaging 2026: Clinical and Biomedical Imaging, 2026
Shape deformation networks for automating aortic valve finite element mesh generation from 3D CT images.
Recommended citation: Qian, L., Chen, J., Gong, R., Sun, W., Liu, M., & Liang, L. (2026). "Shape deformation networks for automated aortic valve finite element meshing from 3D CT images." Proc. SPIE 13929, Medical Imaging 2026. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13929/139291I/Shape-deformation-networks-for-automated-aortic-valve-finite-element-meshing/10.1117/12.3087767.full
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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