Andrew Jesson
Andrew Jesson
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Cited by
Cited by
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ...
arXiv preprint arXiv:1811.02629, 2018
Longitudinal multiple sclerosis lesion segmentation: resource and challenge
A Carass, S Roy, A Jog, JL Cuzzocreo, E Magrath, A Gherman, J Button, ...
NeuroImage 148, 77-102, 2017
Cased: Curriculum adaptive sampling for extreme data imbalance
A Jesson, N Guizard, SH Ghalehjegh, D Goblot, F Soudan, N Chapados
International Conference on Medical Image Computing and Computer-Assisted …, 2017
Brain tumor segmentation using a 3d fcn with multi-scale loss
A Jesson, T Arbel
International MICCAI Brainlesion Workshop, 392-402, 2017
Hierarchical MRF and random forest segmentation of MS lesions and healthy tissues in brain MRI
A Jesson, T Arbel
proceedings of the 2015 longitudinal multiple sclerosis lesion segmentation …, 2015
Evaluating white matter lesion segmentations with refined Sørensen-Dice analysis
A Carass, S Roy, A Gherman, JC Reinhold, A Jesson, T Arbel, O Maier, ...
Scientific reports 10 (1), 1-19, 2020
Attentive task-agnostic meta-learning for few-shot text classification
X Jiang, M Havaei, G Chartrand, H Chouaib, T Vincent, A Jesson, ...
Identifying causal-effect inference failure with uncertainty-aware models
A Jesson, S Mindermann, U Shalit, Y Gal
Advances in Neural Information Processing Systems 33, 2020
On feature collapse and deep kernel learning for single forward pass uncertainty
J van Amersfoort, L Smith, A Jesson, O Key, Y Gal
arXiv preprint arXiv:2102.11409, 2021
Task adaptive metric space for medium-shot medical image classification
X Jiang, L Ding, M Havaei, A Jesson, S Matwin
International Conference on Medical Image Computing and Computer-Assisted …, 2019
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
A Jesson, S Mindermann, Y Gal, U Shalit
ICML, 2021
Adversarially learned mixture model
A Jesson, C Low-Kam, F Soudan, N Chapados
ICML Workshop: Theoretical Foundations and Applications of Deep Generative …, 2018
Method and system for generating synthetically anonymized data for a given task
F Chandelier, A Jesson, M Havaei, L Dijorio, LOWKAM Cevile, ...
US Patent App. 17/259,908, 2021
Brain Lesion Detection and Tumor Segmentation in MRI Using 3D Fully Convolutional Networks
A Jesson
PQDT-Global, 2019
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