no code implementations • 29 Nov 2023 • Yuyang Hu, Satya V. V. N. Kothapalli, Weijie Gan, Alexander L. Sukstanskii, Gregory F. Wu, Manu Goyal, Dmitriy A. Yablonskiy, Ulugbek S. Kamilov
We introduce a new framework called DiffGEPCI for cross-modality generation in magnetic resonance imaging (MRI) using a 2. 5D conditional diffusion model.
no code implementations • 3 Nov 2023 • Chicago Park, Weijie Gan, Zihao Zou, Yuyang Hu, Zhixin Sun, Ulugbek S. Kamilov
There is a growing interest in model-based deep learning (MBDL) for solving imaging inverse problems.
no code implementations • 2 Oct 2023 • Yuyang Hu, Mauricio Delbracio, Peyman Milanfar, Ulugbek S. Kamilov
Image denoisers have been shown to be powerful priors for solving inverse problems in imaging.
no code implementations • 7 Oct 2022 • Weijie Gan, Chunwei Ying, Parna Eshraghi, Tongyao Wang, Cihat Eldeniz, Yuyang Hu, Jiaming Liu, Yasheng Chen, Hongyu An, Ulugbek S. Kamilov
Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data.
no code implementations • 5 Oct 2022 • Yuyang Hu, Weijie Gan, Chunwei Ying, Tongyao Wang, Cihat Eldeniz, Jiaming Liu, Yasheng Chen, Hongyu An, Ulugbek S. Kamilov
However, estimation of accurate CSMs is a challenging problem when measurements are highly undersampled.
no code implementations • 26 Jul 2022 • Shirin Shoushtari, Jiaming Liu, Yuyang Hu, Ulugbek S. Kamilov
While the empirical performance and theoretical properties of DMBAs have been widely investigated, the existing work in the area has primarily focused on their performance when the desired image prior is known exactly.
no code implementations • 10 Feb 2022 • Yuyang Hu, Jiaming Liu, Xiaojian Xu, Ulugbek S. Kamilov
Regularization by denoising (RED) is a widely-used framework for solving inverse problems by leveraging image denoisers as image priors.
no code implementations • 14 Oct 2018 • Haoming Lin, Yuyang Hu, Siping Chen, Jianhua Yao, Ling Zhang
However, CNN in previous studies do not involve cell morphological information, and it is unknown whether morphological features can be directly modeled by CNN to classify cervical cells.