23 citations · 26 across the 3 of their papers we have counts for
7 papers
Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation
Shuai Chen, Antonio Garcia-Uceda, Jiahang Su +4
Deep convolutional neural networks for image segmentation do not learn the label structure explicitly and may produce segmentations with an incorrect structure, e.g., with disconne…
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…
Multi-view analysis of unregistered medical images using cross-view transformers
Gijs van Tulder, Yao Tong, Elena Marchiori
Multi-view medical image analysis often depends on the combination of information from multiple views. However, differences in perspective or other forms of misalignment can make i…
Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentation
Shuai Chen, Gerda Bortsova, Antonio Garcia-Uceda Juarez +2
We propose a novel semi-supervised image segmentation method that simultaneously optimizes a supervised segmentation and an unsupervised reconstruction objectives. The reconstructi…
Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks
Florian Dubost, Hieab Adams, Pinar Yilmaz +6
Finding automatically multiple lesions in large images is a common problem in medical image analysis. Solving this problem can be challenging if, during optimization, the automated…
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
Gerda Bortsova, Gijs van Tulder, Florian Dubost +5
Intracranial carotid artery calcification (ICAC) is a major risk factor for stroke, and might contribute to dementia and cognitive decline. Reliance on time-consuming manual annota…