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researcher

F. Pfeiffer

12 papers hereh-index 13873 citations60 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author8
  • last author3

Across the 11 of 12 papers where every author was matched, so the position is known.

fields
  • eess.IV6
  • cs.CV3
  • physics.med-ph2
  • cs.LG1
same name
  • F. Pfeiffer — 8 papers, h 70
  • F. Pfeiffer — 5 papers, h 7
  • F. Pfeiffer — 5 papers, h 4
  • F. Pfeiffer — 1 paper, h 33
  • F. Pfeiffer — 1 paper, h 16

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20212026
most citedWNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer

48 citations · 79 across the 12 of their papers we have counts for

collaborators
Showing 2023Show all

3 papers · 1 filter

cs.CV2023★ 8 cited

Improving image quality of sparse-view lung tumor CT images with U-Net

Annika Ries, Tina Dorosti, Johannes Thalhammer +8

Background: We aimed at improving image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and determining the best tradeoff be…

eess.IV2023★ 6 cited

Improving Automated Hemorrhage Detection in Sparse-view Computed Tomography via Deep Convolutional Neural Network based Artifact Reduction

Johannes Thalhammer, Manuel Schultheiss, Tina Dorosti +4

This is a preprint. The latest version has been published here: https://pubs.rsna.org/doi/10.1148/ryai.230275 Purpose: Sparse-view computed tomography (CT) is an effective way to r…

eess.IV2023★ 13 cited

Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging

Tina Dorosti, Manuel Schultheiss, Felix Hofmann +7

We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by explorin…

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