182 citations · 341 across the 7 of their papers we have counts for
7 papers
ST-NeRP: Spatial-Temporal Neural Representation Learning with Prior Embedding for Patient-specific Imaging Study
Liang Qiu, Liyue Shen, Lianli Liu +3
During and after a course of therapy, imaging is routinely used to monitor the disease progression and assess the treatment responses. Despite of its significance, reliably capturi…
Part-aware Prompted Segment Anything Model for Adaptive Segmentation
Chenhui Zhao, Liyue Shen
Precision medicine, such as patient-adaptive treatments assisted by medical image analysis, poses new challenges for segmentation algorithms in adapting to new patients, due to the…
Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency
Bowen Song, Soo Min Kwon, Zecheng Zhang +3
Diffusion models have recently emerged as powerful generative priors for solving inverse problems. However, training diffusion models in the pixel space are both data-intensive and…
Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior
Zongyu Li, Jason Hu, Xiaojian Xu +2
Phase retrieval (PR) is a crucial problem in many imaging applications. This study focuses on resolving the holographic phase retrieval problem in situations where the measurements…
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging
Rui Yan, Liangqiong Qu, Qingyue Wei +5
The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder da…
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
Yang Song, Liyue Shen, Lei Xing +1
Reconstructing medical images from partial measurements is an important inverse problem in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Existing solutions based o…
NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction
Liyue Shen, John Pauly, Lei Xing
Image reconstruction is an inverse problem that solves for a computational image based on sampled sensor measurement. Sparsely sampled image reconstruction poses addition challenge…