187 citations · 721 across the 60 of their papers we have counts for
5 papers · 1 filter
A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation
Robin Camarasa, Daniel Bos, Jeroen Hendrikse +4
Uncertainty assessment has gained rapid interest in medical image analysis. A popular technique to compute epistemic uncertainty is the Monte-Carlo (MC) dropout technique. From a n…
Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
Alain Lalande, Zhihao Chen, Thibaut Pommier +30
A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization the…
Automated Segmentation and Volume Measurement of Intracranial Carotid Artery Calcification on Non-Contrast CT
Gerda Bortsova, Daniel Bos, Florian Dubost +4
Purpose: To evaluate a fully-automated deep-learning-based method for assessment of intracranial carotid artery calcification (ICAC). Methods: Two observers manually delineated ICA…
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…
Automatic airway segmentation from Computed Tomography using robust and efficient 3-D convolutional neural networks
A. Garcia-Uceda, R. Selvan, Z. Saghir +2
This paper presents a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. We use a simple and low…