no code implementations • 25 Sep 2023 • Qingjie Meng, Wenjia Bai, Declan P O'Regan, and Daniel Rueckert
We propose a novel learning framework, DeepMesh, which propagates a template heart mesh to a subject space and estimates the 3D motion of the heart mesh from CMR images for individual subjects.
no code implementations • 5 Sep 2022 • Qingjie Meng, Wenjia Bai, Tianrui Liu, Declan P O'Regan, Daniel Rueckert
By developing a differentiable mesh-to-image rasterizer, the method is able to leverage the anatomical shape information from 2D multi-view CMR images for 3D motion estimation.
no code implementations • 29 Jul 2022 • Qingjie Meng, Chen Qin, Wenjia Bai, Tianrui Liu, Antonio de Marvao, Declan P O'Regan, Daniel Rueckert
To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart.
no code implementations • 19 Jun 2021 • Tianrui Liu, Qingjie Meng, Jun-Jie Huang, Athanasios Vlontzos, Daniel Rueckert, Bernhard Kainz
Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content while removing redundant video frames.
no code implementations • 30 Oct 2020 • Qingjie Meng, Jacqueline Matthew, Veronika A. Zimmer, Alberto Gomez, David F. A. Lloyd, Daniel Rueckert, Bernhard Kainz
To address this problem, we propose Mutual Information-based Disentangled Neural Networks (MIDNet), which extract generalizable categorical features to transfer knowledge to unseen categories in a target domain.
1 code implementation • 19 Aug 2020 • Qingjie Meng, Daniel Rueckert, Bernhard Kainz
The proposed MetFA method explicitly and directly learns the latent representation without using domain adversarial training.
no code implementations • 16 Aug 2020 • Jeremy Tan, Anselm Au, Qingjie Meng, Sandy FinesilverSmith, John Simpson, Daniel Rueckert, Reza Razavi, Thomas Day, David Lloyd, Bernhard Kainz
In this paper we discuss the potential for deep learning techniques to aid in the detection of congenital heart disease (CHD) in fetal ultrasound.
1 code implementation • 19 May 2020 • Tianrui Liu, Qingjie Meng, Athanasios Vlontzos, Jeremy Tan, Daniel Rueckert, Bernhard Kainz
We show that our method is superior to alternative video summarization methods and that it preserves essential information required by clinical diagnostic standards.
no code implementations • 29 Feb 2020 • Qingjie Meng, Daniel Rueckert, Bernhard Kainz
Deep learning models exhibit limited generalizability across different domains.
no code implementations • 30 Aug 2019 • Jeremy Tan, Anselm Au, Qingjie Meng, Bernhard Kainz
Semi-supervised learning methods have achieved excellent performance on standard benchmark datasets using very few labelled images.
1 code implementation • 21 Aug 2019 • Qingjie Meng, Nick Pawlowski, Daniel Rueckert, Bernhard Kainz
These entangled image properties lead to a semantically redundant feature encoding for the relevant task and thus lead to poor generalization of deep learning algorithms.
no code implementations • 20 Nov 2018 • Qingjie Meng, Matthew Sinclair, Veronika Zimmer, Benjamin Hou, Martin Rajchl, Nicolas Toussaint, Ozan Oktay, Jo Schlemper, Alberto Gomez, James Housden, Jacqueline Matthew, Daniel Rueckert, Julia Schnabel, Bernhard Kainz
Our method is more consistent than human annotation, and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions.