Hugging Face Trending Papers
Oct 13

Noise2Match: Self-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching

Noise2Match is a self‑supervised method for low‑dose CT denoising that uses an adaptive inter‑slice matching network to model and correct the label transition between adjacent slices. By ensuring the equal‑mean condition of the Noise2Noise framework, it theoretically matches supervised training with paired noisy‑clean data. The approach also extends effectively to low‑dose cone‑beam CT, outperforming existing image‑domain self‑supervised methods.

arXiv AI
Jul 31

PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imaging

arXiv:2602. 21987v3 Announce Type: replace-cross Abstract: Low-dose CT images are essential for reducing radiation exposure in cancer screening, pediatric imaging, and longitudinal monitoring protocols, but their quality is often degraded by noise from low-dose acquisition, patient motion, or scanner limitations, affecting both clinical interpretation and downstream analysis.

By Jitindra Fartiyal, Pedro Freire, Sergei K. Turitsyn, Sergei G. Solovski
arXiv AI
Jul 7

Efficient Flow Matching for Sparse-View CT Reconstruction

arXiv:2603. 00205v2 Announce Type: replace-cross Abstract: Generative models, particularly Diffusion Models (DM), have shown strong potential for Computed Tomography (CT) reconstruction serving as expressive priors for solving ill-posed inverse problems.

By Jiayang Shi, Lincen Yang, Zhong Li, Tristan van Leeuwen, Daniel M. Pelt, K. Joost Batenburg