activity
20232025
most citedRing Artifact Correction in Photon-Counting Spectral CT Using a Convolutional Neural Network With Spectral Loss

4 citations · 8 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2025

Poisson Flow Consistency Training

Anthony Zhang, Mahmut Gokmen, Dennis Hein +4

The Poisson Flow Consistency Model (PFCM) is a consistency-style model based on the robust Poisson Flow Generative Model++ (PFGM++) which has achieved success in unconditional imag…

cs.CV2024★ 1 cited

CheXalign: Preference fine-tuning in chest X-ray interpretation models without human feedback

Dennis Hein, Zhihong Chen, Sophie Ostmeier +8

Radiologists play a crucial role in translating medical images into actionable reports. However, the field faces staffing shortages and increasing workloads. While automated approa…

eess.IV2024★ 1 cited

Physics-Inspired Generative Models in Medical Imaging: A Review

Dennis Hein, Afshin Bozorgpour, Dorit Merhof +1

Physics-inspired Generative Models (GMs), in particular Diffusion Models (DMs) and Poisson Flow Models (PFMs), enhance Bayesian methods and promise great utility in medical imaging…

eess.IV2024★ 1 cited

PFCM: Poisson flow consistency models for low-dose CT image denoising

Dennis Hein, Grant Stevens, Adam Wang +1

X-ray computed tomography (CT) is widely used for medical diagnosis and treatment planning; however, concerns about ionizing radiation exposure drive efforts to optimize image qual…

eess.IV2023★ 1 cited

PPFM: Image denoising in photon-counting CT using single-step posterior sampling Poisson flow generative models

Dennis Hein, Staffan Holmin, Timothy Szczykutowicz +4

Diffusion and Poisson flow models have shown impressive performance in a wide range of generative tasks, including low-dose CT image denoising. However, one limitation in general,…

physics.med-ph2023

Noise suppression in photon-counting CT using unsupervised Poisson flow generative models

Dennis Hein, Staffan Holmin, Timothy Szczykutowicz +4

Deep learning has proven to be important for CT image denoising. However, such models are usually trained under supervision, requiring paired data that may be difficult to obtain i…

physics.med-ph2023★ 4 cited

Ring Artifact Correction in Photon-Counting Spectral CT Using a Convolutional Neural Network With Spectral Loss

Dennis Hein, Konstantinos Liappis, Fredrik Grönberg +2

Photon-counting spectral computed tomography is now clinically available. These new detectors come with the promise of higher contrast-to-noise ratio and spatial resolution and imp…