output
19932026
most citedDeep learning for cardiac image segmentation: A review

870 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing cs.CVShow all

5 papers · 1 filter

cs.CV2025★ 4 cited

The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning

Akis Linardos, Sarthak Pati, Ujjwal Baid +25

We present the design and results of the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024, which focuses on federated learning (FL) for glioma sub-region segmentation in m…

cs.CV2020★ 6 cited

Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey

O. Kechagias-Stamatis, N. Aouf

Automatic Target Recognition (ATR) for military applications is one of the core processes towards enhancing intelligencer and autonomously operating military platforms. Spurred by…

cs.CV2020★ 19 cited

Robust On-Manifold Optimization for Uncooperative Space Relative Navigation with a Single Camera

Duarte Rondao, Nabil Aouf, Mark A. Richardson +1

Optical cameras are gaining popularity as the suitable sensor for relative navigation in space due to their attractive sizing, power and cost properties when compared to convention…

cs.CV2019★ 3 cited

Single and Cross-Dimensional Feature Detection and Description: An Evaluation

Odysseas Kechagias-Stamatis, Nabil Aouf, Mark A. Richardson

Three-dimensional local feature detection and description techniques are widely used for object registration and recognition applications. Although several evaluations of 3D local…

cs.CV2018★ 4 cited

Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models

Nathan J Olliverre, Guang Yang, Gregory Slabaugh +2

Magnetic Resonance Spectroscopy (MRS) provides valuable information to help with the identification and understanding of brain tumors, yet MRS is not a widely available medical ima…