activity
20172022
most citedTowards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology

70 citations · 104 across the 11 of their papers we have counts for

collaborators

15 papers

cs.CV2022★ 1 cited

OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data

David Schinagl, Georg Krispel, Horst Possegger +2

While 3D object detection in LiDAR point clouds is well-established in academia and industry, the explainability of these models is a largely unexplored field. In this paper, we pr…

eess.IV2021★ 2 cited

Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations

Antonio Pepe, Jan Egger, Marina Codari +7

Objective: Surveillance imaging of chronic aortic diseases, such as dissections, relies on obtaining and comparing cross-sectional diameter measurements at predefined aortic landma…

cs.CV2020

Geometric Correspondence Fields: Learned Differentiable Rendering for 3D Pose Refinement in the Wild

Alexander Grabner, Yaming Wang, Peizhao Zhang +5

We present a novel 3D pose refinement approach based on differentiable rendering for objects of arbitrary categories in the wild. In contrast to previous methods, we make two main…

cs.CV2020★ 10 cited

ALCN: Adaptive Local Contrast Normalization

Mahdi Rad, Peter M. Roth, Vincent Lepetit

To make Robotics and Augmented Reality applications robust to illumination changes, the current trend is to train a Deep Network with training images captured under many different…

cs.LG2019

Performing Arithmetic Using a Neural Network Trained on Digit Permutation Pairs

Marcus D. Bloice, Peter M. Roth, Andreas Holzinger

In this paper a neural network is trained to perform simple arithmetic using images of concatenated handwritten digit pairs. A convolutional neural network was trained with images…

cs.CV2019★ 2 cited

Patch augmentation: Towards efficient decision boundaries for neural networks

Marcus D. Bloice, Peter M. Roth, Andreas Holzinger

In this paper we propose a new augmentation technique, called patch augmentation, that, in our experiments, improves model accuracy and makes networks more robust to adversarial at…