Publications
PhD thesis:
- Understanding, Modeling and Detecting Brain Tumors: Graphical Models and Concurrent Segmentation/Registration methods. Sarah Parisot, November 2013. [PDF]
2023
Eli Verwimp, Kuo Yang, Sarah Parisot, Lanqing Hong, Steven McDonagh, Eduardo Pérez-Pellitero, Matthias De Lange, Tinne Tuytelaars: Clad: A realistic continual learning benchmark for autonomous driving. Neural Networks. [Code] [PDF]
Sarah Parisot, Yongxin Yang, Steven McDonagh: Learning to Name Classes for Vision and Language Models. CVPR 2023. [Video] [PDF]
2022
William Thong, Jose Costa Pereira, Sarah Parisot, Ales Leonardis, Steven McDonagh: Content-Diverse Comparisons improve IQA. BMVC 2022. [Paper Page]
Eli Verwimp, Kuo Yang, Sarah Parisot, Hong Lanqing, Steven McDonagh, Eduardo Pérez-Pellitero, Matthias De Lange, Tinne Tuytelaars: Re-examining distillation for continual object detection. BMVC 2022. [Paper Page]
Sarah Parisot, Pedro M Esperança, Steven McDonagh, Tamas J Madarasz, Yongxin Yang, Zhenguo Li: Long-tail recognition via compositional knowledge transfer. CVPR 2022. [PDF]
2021
Mateusz Michalkiewicz, Stavros Tsogkas, Sarah Parisot, Mahsa Baktashmotlagh, Anders Eriksson, Eugene Belilovsky: Learning Compositional Shape Priors for Few-Shot 3D Reconstruction. ArXiv. [PDF]
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh, Tinne Tuytelaars : A continual learning survey: Defying forgetting in classification tasks. PAMI. [Code] [PDF]
2020
Carlo Biffi, Steven McDonagh, Philip Torr, Ales Leonardis, Sarah Parisot: Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection. ECCV 2020. [Video] [PDF]
Yu Liu, Sarah Parisot, Gregory Slabaugh, Xu Jia, Ales Leonardis, Tinne Tuytelaars: More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning. ECCV 2020. [Code] [PDF]
Mateusz Michalkiewicz, Sarah Parisot, Stavros Tsogkas, Mahsa Baktashmotlagh, Anders Eriksson, Eugene Belilovsky: Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors. ECCV 2020. [Video][Code] [PDF]
Danai Triantafyllidou, Sean Moran, Steven McDonagh, Sarah Parisot, Gregory Slabaugh: Low light video Enhancement using Synthetic Data Produced with an Intermediate Domain Mapping. ECCV 2020. [PDF]
Daniel Hernandez, Sarah Parisot, Ales Leonardis, Gregory Slabaugh, Steven McDonagh. A multi-hypothesis approach to color constancy. CVPR 2020. [Code] [PDF]
Sean Moran, Pierre Marza, Steven McDonagh, Sarah Parisot, Gregory Slabaugh. Deep local parametric filters for imageenhancement. CVPR 2020. [Code] [PDF]
Matthias De Lange, Xu Jia, Sarah Parisot, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars. Unsupervised model person-alization while preserving privacy and scalability: an open problem. CVPR 2020. [Code] [PDF]
Katarina Tothova, Sarah Parisot, Matthew Lee, Esther Puyol Anton, Andrew King, Marc Pollefeys, Ender Konukoglu. Probabilistic 3D surface reconstruction from sparse MRI information. MICCAI 2020.
Linpu Fang, Hang Xu, Zhili Liu, Sarah Parisot, Zhenguo Li: EHSOD: CAM-Guided End-to-end Hybrid-Supervised ObjectDetection with Cascade Refinement. AAAI 2020. [PDF]
2019
*Steven McDonagh, *Sarah Parisot, Fengwei Zhou, Xing Zhang, Ales Leonardis, Zhenguo Li, Gregory Slabaugh: Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem. Preprint. [PDF]
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars: Continual learning: A comparative study on how to defy forgetting in classification tasks. Preprint. [Code] [PDF]
2018
Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot: Learning Conditioned Graph Structures for Interpretable Visual Question Answering. NeurIPS 2018. [Code] [PDF]
Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrerro, Ben Glocker, Daniel Rueckert: Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer’s Disease. Medical Image Analysis, invited paper for MICCAI 2017 special issue. [Code] [PDF]
Katarína Tóthová, Sarah Parisot, Matthew CH Lee, Esther Puyol-Antón, Lisa M. Koch, Andrew P. King, Ender Konukoglu, and Marc Pollefeys. “Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors.” SHAPEMI@MICCAI 2018. [PDF]
2017
Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante, Martin Rajchl, Matthew Lee, Ben Glocker, Daniel Rueckert: Metric Learning with Spectral Graph Convolutions on Brain Connectivity Networks. NeuroImage (2017) [Code]
Sarah Parisot, Ben Glocker, Sofia Ira Ktena, Salim Arslan, Markus D Schirmer, Daniel Rueckert: A flexible graphical model for multi-modal parcellation of the cortex. NeuroImage (2017) [DOI] [PDF]
Salim Arslan, Sofia Ira Ktena, Antonios Makropoulos, Emma C. Robinson, Daniel Rueckert, Sarah Parisot: Human Brain Mapping: A Systematic Comparison of Parcellation Methods for the Human Cerebral Cortex. NeuroImage (2017) [Code and Data] [DOI] [PDF]
Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrerro, Ben Glocker, Daniel Rueckert: Spectral Graph Convolutions for Population-based Disease Prediction. MICCAI 2017. [Code] [PDF]
Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante, Martin Rajchl, Matthew Lee, Ben Glocker, Daniel Rueckert: Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks. MICCAI 2017. [Code] [PDF]
Sofia Ira Ktena, Salim Arslan, Sarah Parisot, Daniel Rueckert: Exploring Heritability of Functional Brain Networks with Inexact Graph Matching. ISBI 2017. [PDF]
Jonathan Passerat-Palmbach, Romain Reuillon, Mathieu Leclaire, Antonios Makropoulos, Emma C Robinson, Sarah Parisot, Daniel Rueckert: Reproducible large-scale neuroimaging studies with the OpenMOLE workflow management system. Frontiers in Neuroinformatics 2017; 11: 21. [DOI][PDF]
2016
Sarah Parisot, Salim Arslan, Jonathan Passerat-Palmbach, William M. Wells III, Daniel Rueckert: Group-wise parcellation of the cortex through multi-scale spectral clustering. NeuroImage (2016). [DOI] [Bibtex][PDF]
Sarah Parisot, Amélie Darlix, Cédric Baumann, Sonia Zouaoui, Yordanka Yordanova, Marie Blonski, Valérie Rigau et al.: A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain. PloS one 11, no. 1 (2016). [DOI] [Bibtex]
Nikos Paragios, Enzo Ferrante, Ben Glocker, Nikos Komodakis, Sarah Parisot, Evangelia I. Zacharaki: (Hyper)-graphical models in biomedical image analysis Medical Image Analysis 33, 102-106 (Invited paper) [DOI][Bibtex]
Sarah Parisot, Ben Glocker, Markus D. Schirmer, and Daniel Rueckert: GraMPa: Graph-Based Multi-modal Parcellation of the Cortex Using Fusion Moves. MICCAI 2016: 148-156. [DOI][Bibtex][PDF]
Salim Arslan, Sarah Parisot, and Daniel Rueckert: Boundary Mapping Through Manifold Learning for Connectivity-Based Cortical Parcellation. MICCAI 2016: 115-122. [DOI][Bibtex]
Sofia Ira Ktena, Sarah Parisot, Jonathan Passerat-Palmbach, Daniel Rueckert: Comparison of Brain Networks with Unknown Correspondences MICCAI Workshop on Brain Analysis using Connectivity Networks (BACON) 2016. [PDF]
Konstantinos Kamnitsas, Enzo Ferrante, Sarah Parisot, Christian Ledig, Aditya Nori, Antonio Criminisi, Daniel Rueckert, Ben Glocker: DeepMedic for Brain Tumor Segmentation BRATS-MICCAI Workshop 2016.
Sarah Parisot and Daniel Rueckert: A Cortical Parcellation Framework for Multimodal Analysis. OHBM, Geneva, 2016.
2015
Sarah Parisot, Martin Rajchl, Jonathan Passerat-Palmbach, Daniel Rueckert: A Continuous Flow-Maximisation Approach to Connectivity-driven Cortical Parcellation. MICCAI (3) 2015: 165-172. [DOI] [Bibtex][PDF][Teaser]
Salim Arslan, Sarah Parisot, Daniel Rueckert: Joint Spectral Decomposition for the Parcellation of the Human Cerebral Cortex Using Resting-State fMRI. IPMI 2015: 85-97 (Oral presentation) [DOI] [Bibtex]
Sarah Parisot, Salim Arslan, Jonathan Passerat-Palmbach, William M. Wells III, Daniel Rueckert: Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex. IPMI 2015: 600-612 [Code][DOI] [Bibtex] [PDF]
Wenjia Bai, Devis Peressutti, Sarah Parisot, Ozan Oktay, Martin Rajchl, Declan P. O’Regan, Stuart A. Cook, Andrew P. King, Daniel Rueckert: Beyond the AHA 17-Segment Model: Motion-Driven Parcellation of the Left Ventricle. STACOM@MICCAI 2015: 13-20 [DOI]
Salim Arslan, Sarah Parisot, and Daniel Rueckert: Supervertex clustering of the cerebral cortex using resting-state fMRI. OHBM, Honolulu, 2015.
Salim Arslan, Sarah Parisot, and Daniel Rueckert: How to represent subregions in a parcellated brain for fMRI analysis? OHBM, Honolulu, 2015.
Sarah Parisot and Daniel Rueckert: Multi-Scale Spectral Parcellation of the Cortex Based on Structural Connectivity. OHBM, Honolulu, 2015. [Poster]
Sarah Parisot and D. Rueckert: Evaluation Methods for Diffusion-driven Cortex Parcellation. OHBM, Honolulu, 2015. [Poster]
Salim Arslan, Sarah Parisot, and Daniel Rueckert: Comparing connectivity-based groupwise parcellations generated from resting-state fMRI and DTI data: Preliminary results. Symposium on Big Data Initiatives for Connectomics Research, BIH 2015.
2014
- Sarah Parisot, William M. Wells III, Stéphane Chemouny, Hugues Duffau, Nikos Paragios: Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs. Medical Image Analysis 18(4): 647-659 (2014) [DOI] [Bibtex] [PDF]
2013
- Sarah Parisot, William M. Wells III, Stéphane Chemouny, Hugues Duffau, Nikos Paragios: Uncertainty-Driven Efficiently-Sampled Sparse Graphical Models for Concurrent Tumor Segmentation and Atlas Registration. ICCV 2013: 641-648 [DOI] [Bibtex] [PDF]
2012
Sarah Parisot, Hugues Duffau, Stéphane Chemouny, Nikos Paragios: Graph-based detection, segmentation & characterization of brain tumors. CVPR 2012: 988-995 [DOI] [Bibtex] [PDF]
Sarah Parisot, Hugues Duffau, Stéphane Chemouny, Nikos Paragios: Joint Tumor Segmentation and Dense Deformable Registration of Brain MR Images. MICCAI (2) 2012: 651-658 (Oral presentation) [DOI] [Bibtex] [PDF] [Slides]