Liyue Shen

Fractional-gradient Sparsity with Autoencoding Sequential Deep Image Prior for 3D CT Reconstruction

Haijie Yuan, Chaoyan Huang, Srijita Bandopadhyay, Liyue Shen et autres

3D volumetric reconstruction from incomplete or noisy measurements is a fundamental problem in medical imaging and computational tomography. Deep image prior (DIP)-based methods have recently shown strong capability for solving inverse problems without requiring large training datasets. However, directly extending DIP to …

Fractional-gradient Sparsity with Autoencoding Sequential Deep Image Prior for 3D CT Reconstruction

Haijie Yuan, Chaoyan Huang, Srijita Bandopadhyay, Liyue Shen et autres

3D volumetric reconstruction from incomplete or noisy measurements is a fundamental problem in medical imaging and computational tomography. Deep image prior (DIP)-based methods have recently shown strong capability for solving inverse problems without requiring large training datasets. However, directly extending DIP to …

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NAMD: Virtual Follow-up Computed Tomography Synthesis via Nodule-Aligned Multimodal Diffusion Models for Early Lung Cancer Diagnosis

James Song, Yifan Wang, Chuan Zhou, Liyue Shen

Lung cancer remains the leading cause of cancer-related mortality worldwide, with survival outcomes critically dependent on early and accurate detection. When low-dose computed tomography (LDCT) findings are indeterminate, clinicians typically defer diagnosis pending follow-up CT imaging obtained up to 12 months later, …

NAMD: Virtual Follow-up Computed Tomography Synthesis via Nodule-Aligned Multimodal Diffusion Models for Early Lung Cancer Diagnosis

James Song, Yifan Wang, Chuan Zhou, Liyue Shen

Lung cancer remains the leading cause of cancer-related mortality worldwide, with survival outcomes critically dependent on early and accurate detection. When low-dose computed tomography (LDCT) findings are indeterminate, clinicians typically defer diagnosis pending follow-up CT imaging obtained up to 12 months later, …

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ST-NeRP: Spatial–temporal neural representation learning with prior embedding for patient-specific imaging study

Liyue Shen, Lianli Liu, Junyan Liu, Yizheng Chen et autres

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Single-Spoke Motion-Compensated Dynamic 3D MRI Reconstruction via Neural Representation

Lixuan Chen, James M. Balter, Liyue Shen, Jeong Joon Park

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Antithetic Noise in Diffusion Models

Jing Jia, Sifan Liu, Bowen Song, Wei Yuan et autres

We systematically study antithetic initial noise in diffusion models, discovering that pairing each noise sample with its negation consistently produces strong negative correlation. This universal phenomenon holds across datasets, model architectures, conditional and unconditional sampling, and even other generative models such as …

TempA-VLP: Temporal-Aware Vision-Language Pretraining for Longitudinal Exploration in Chest X-Ray Image

Zhuoyi Yang, Liyue Shen

Longitudinal medical image processing is a significant task to understand the dynamic changes of disease by taking and comparing image series over time, providing insights into how conditions evolve and enabling more accurate di-agnosis and treatment planning. While recent advance-ments in biomedical …

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CCS: Controllable and Constrained Sampling with Diffusion Models via Initial Noise Perturbation

Bowen Song, Zecheng Zhang, Zhaoxu Luo, Jason Hu et autres

Diffusion models have emerged as powerful tools for generative tasks, producing high-quality outputs across diverse domains. However, how the generated data responds to the initial noise perturbation in diffusion models remains under-explored, which hinders understanding the controllability of the sampling process. In …

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Test-Time Adaptation Improves Inverse Problem Solving With Patch-Based Diffusion Models

Jason Hu, Bowen Song, Jeffrey A. Fessler, Liyue Shen

Diffusion models have achieved excellent success in solving inverse problems due to their ability to learn strong image priors, but existing approaches require a large training dataset of images that should come from the same distribution as the test dataset. In practice, …

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CoSIGN: Few-Step Guidance of ConSIstency Model to Solve General INverse Problems

Jiankun Zhao, Liyue Shen

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Latent Space Disentanglement in Diffusion Transformers Enables Precise Zero-shot Semantic Editing

Zitao Shuai, Chenwei Wu, Zhengxu Tang, Bowen Song et autres

Diffusion Transformers (DiTs) have recently achieved remarkable success in text-guided image generation. In image editing, DiTs project text and image inputs to a joint latent space, from which they decode and synthesize new images. However, it remains largely unexplored how multimodal information …