187 citations · 321 across the 6 of their papers we have counts for
6 papers
World of Forms: Deformable Geometric Templates for One-Shot Surface Meshing in Coronary CT Angiography
Rudolf L. M. van Herten, Ioannis Lagogiannis, Jelmer M. Wolterink +8
Deep learning-based medical image segmentation and surface mesh generation typically involve a sequential pipeline from image to segmentation to meshes, often requiring large train…
COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification
L. E. Hogeweg, R. Gangireddy, D. Brunink +3
High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper, we fo…
Adversarial Heart Attack: Neural Networks Fooled to Segment Heart Symbols in Chest X-Ray Images
Gerda Bortsova, Florian Dubost, Laurens Hogeweg +2
Adversarial attacks consist in maliciously changing the input data to mislead the predictions of automated decision systems and are potentially a serious threat for automated medic…
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors
Gerda Bortsova, Cristina González-Gonzalo, Suzanne C. Wetstein +9
Adversarial attacks are considered a potentially serious security threat for machine learning systems. Medical image analysis (MedIA) systems have recently been argued to be vulner…
Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations
Gerda Bortsova, Florian Dubost, Laurens Hogeweg +2
The scarcity of labeled data often limits the application of supervised deep learning techniques for medical image segmentation. This has motivated the development of semi-supervis…
Deep Learning from Label Proportions for Emphysema Quantification
Gerda Bortsova, Florian Dubost, Silas Ørting +5
We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts…