44 citations · 64 across the 5 of their papers we have counts for
5 papers
Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder
Matan Atad, David Schinz, Hendrik Moeller +6
Counterfactual explanations (CEs) aim to enhance the interpretability of machine learning models by illustrating how alterations in input features would affect the resulting predic…
Numerical simulation of individual coil placement -- A proof-of-concept study for the prediction of recurrence after aneurysm coiling
Julian Schwarting, Fabian Holzberger, Markus Muhr +4
Rupture of intracranial aneurysms results in severe subarachnoidal hemorrhage, which is associated with high morbidity and mortality. Neurointerventional occlusion of the aneurysm…
3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images
Alina F. Dima, Veronika A. Zimmer, Martin J. Menten +8
Automated segmentation of the blood vessels in 3D volumes is an essential step for the quantitative diagnosis and treatment of many vascular diseases. 3D vessel segmentation is bei…
Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation
Robert Graf, Joachim Schmitt, Sarah Schlaeger +8
Background: Automated segmentation of spinal MR images plays a vital role both scientifically and clinically. However, accurately delineating posterior spine structures presents ch…
CheXplaining in Style: Counterfactual Explanations for Chest X-rays using StyleGAN
Matan Atad, Vitalii Dmytrenko, Yitong Li +6
Deep learning models used in medical image analysis are prone to raising reliability concerns due to their black-box nature. To shed light on these black-box models, previous works…