Maxime Sermesant

Computer Vision and Pattern Recognition · 2005–2020

A researcher in the Science corpus: 7 papers; trusts 3 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 29 citations into it; #16,777 of 21,575 by trust here, #14,478 by citations

Whom Maxime Sermesant trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. William E. Lorensen

Who trusts Maxime Sermesant overall

1. Aristeidis Sotiras

Papers from Maxime Sermesant's perspective

Ordered by how strongly the researchers Maxime Sermesant trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet: A large-scale hierarchical image database
2. Fully convolutional networks for semantic segmentation
3. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
4. Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms (International Journal of Computer Vision)
5. Learning Spatiotemporal Features with 3D Convolutional Networks
6. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation (IEEE Transactions on Medical Imaging)
7. Block Matching: A General Framework to Improve Robustness of Rigid Registration of Medical Images (International Conference on Medical Image Computing and Computer-Assisted Intervention)
8. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
9. The Elements of Statistical Learning
10. Marching cubes: A high resolution 3D surface construction algorithm

Their papers

• Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation (2005) — 375 citations
• A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging (2020) — 478 citations
• Model-Based Generation of Large Databases of Cardiac Images: Synthesis of Pathological Cine MR Sequences From Real Healthy Cases (2018) — 47 citations
• Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? (2018) — 2,285 citations
• A Framework for the Generation of Realistic Synthetic Cardiac Ultrasound and Magnetic Resonance Imaging Sequences From the Same Virtual Patients (2018) — 41 citations
• Statistical shape modeling of the left ventricle: myocardial infarct classification challenge (2017) — 91 citations
• SVF-Net: Learning Deformable Image Registration Using Shape Matching (2017) — 329 citations