1 code implementation • 25 Sep 2023 • Yuning Du, Yuyang Xue, Rohan Dharmakumar, Sotirios A. Tsaftaris
Deep learning (DL) reconstruction particularly of MRI has led to improvements in image fidelity and reduction of acquisition time.
no code implementations • 25 Aug 2023 • Dilek M. Yalcinkaya, Khalid Youssef, Bobak Heydari, Orlando Simonetti, Rohan Dharmakumar, Subha Raman, Behzad Sharif
In the proposed approach, we referred the top 10% most uncertain segmentations as detected by our dQC tool to the human expert for refinement.
1 code implementation • 5 Sep 2020 • Haochuan Jiang, Agisilaos Chartsias, Xinheng Zhang, Giorgos Papanastasiou, Scott Semple, Mark Dweck, David Semple, Rohan Dharmakumar, Sotirios A. Tsaftaris
The model is trained in a semi-supervised fashion with new reconstruction losses directly aiming to improve pathology segmentation with limited annotations.
2 code implementations • 11 Nov 2019 • Agisilaos Chartsias, Giorgos Papanastasiou, Chengjia Wang, Scott Semple, David E. Newby, Rohan Dharmakumar, Sotirios A. Tsaftaris
Core to our method is learning a disentangled decomposition into anatomical and imaging factors.
4 code implementations • 22 Mar 2019 • Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou, Michelle Williams, David Newby, Rohan Dharmakumar, Sotirios A. Tsaftaris
We can venture further and consider that a medical image naturally factors into some spatial factors depicting anatomy and factors that denote the imaging characteristics.
1 code implementation • 19 Mar 2018 • Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou, Scott Semple, Michelle Williams, David Newby, Rohan Dharmakumar, Sotirios A. Tsaftaris
Specifically, we achieve comparable performance to fully supervised networks using a fraction of labelled images in experiments on ACDC and a dataset from Edinburgh Imaging Facility QMRI.