3 papers
eess.IV2023
Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction
Bo Zhou, Jo Schlemper, Neel Dey +5
While enabling accelerated acquisition and improved reconstruction accuracy, current deep MRI reconstruction networks are typically supervised, require fully sampled data, and are…
cs.CV2018
Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection
Seyed Raein Hashemi, Seyed Sadegh Mohseni Salehi, Deniz Erdogmus +3
Fully convolutional deep neural networks have been asserted to be fast and precise frameworks with great potential in image segmentation. One of the major challenges in training su…
cs.CV2018
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration
Seyed Sadegh Mohseni Salehi, Shadab Khan, Deniz Erdogmus +1
With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-to-template 3D registration, we propose deep learning-based m…