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

no code implementations • 16 Mar 2023 • Johannes Thalhammer, Manuel Schultheiss, Tina Dorosti, Tobias Lasser, Franz Pfeiffer, Daniela Pfeiffer, Florian Schaff

Purpose: Sparse-view computed tomography (CT) is an effective way to reduce dose by lowering the total number of views acquired, albeit at the expense of image quality, which, in turn, can impact the ability to detect diseases.

Computed Tomography (CT)

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

no code implementations • 13 Mar 2023 • Tina Dorosti, Manuel Schultheiss, Felix Hofmann, Johannes Thalhammer, Luisa Kirchner, Theresa Urban, Franz Pfeiffer, Florian Schaff, Tobias Lasser, Daniela Pfeiffer

Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0. 86 [0. 82, 0. 89].

Binary Classification Computed Tomography (CT)

Per-Pixel Lung Thickness and Lung Capacity Estimation on Chest X-Rays using Convolutional Neural Networks

no code implementations • 24 Oct 2021 • Manuel Schultheiss, Philipp Schmette, Thorsten Sellerer, Rafael Schick, Kirsten Taphorn, Korbinian Mechlem, Lorenz Birnbacher, Bernhard Renger, Marcus R. Makowski, Franz Pfeiffer, Daniela Pfeiffer

Additionally, we predicted the lung thicknesses on the synthetic test set, where the mean-absolute error between the total volumes was 0. 19 liter with a positive correlation (r = 0. 99).

Capacity Estimation