271 citations · 320 across the 6 of their papers we have counts for
6 papers
Right Regions, Wrong Labels: Semantic Label Flips in Segmentation under Correlation Shift
Akshit Achara, Yovin Yahathugoda, Nick Byrne +4
The robustness of machine learning models can be compromised by spurious correlations between non-causal features in the input data and target labels. A common way to test for such…
A persistent homology-based topological loss for CNN-based multi-class segmentation of CMR
Nick Byrne, James R Clough, Isra Valverde +2
Multi-class segmentation of cardiac magnetic resonance (CMR) images seeks a separation of data into anatomical components with known structure and configuration. The most popular C…
A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI
Nick Byrne, James R. Clough, Giovanni Montana +1
With respect to spatial overlap, CNN-based segmentation of short axis cardiovascular magnetic resonance (CMR) images has achieved a level of performance consistent with inter obser…
A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology
James R. Clough, Nicholas Byrne, Ilkay Oksuz +3
We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly pro…
Topology-preserving augmentation for CNN-based segmentation of congenital heart defects from 3D paediatric CMR
Nick Byrne, James R. Clough, Isra Valverde +2
Patient-specific 3D printing of congenital heart anatomy demands an accurate segmentation of the thin tissue interfaces which characterise these diagnoses. Even when a label set ha…
Explicit topological priors for deep-learning based image segmentation using persistent homology
James R. Clough, Ilkay Oksuz, Nicholas Byrne +2
We present a novel method to explicitly incorporate topological prior knowledge into deep learning based segmentation, which is, to our knowledge, the first work to do so. Our meth…