Andrew Jesson
Andrew Jesson
Vahvistettu sähköpostiosoite verkkotunnuksessa - Kotisivu
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
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
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
Brain tumor segmentation using a 3D FCN with multi-scale loss
A Jesson, T Arbel
International MICCAI Brainlesion Workshop, 392-402, 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
Identifying causal-effect inference failure with uncertainty-aware models
A Jesson*, S Mindermann*, U Shalit, Y Gal
Advances in Neural Information Processing Systems (NeurIPS) 33, 2020
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
Attentive task-agnostic meta-learning for few-shot text classification
X Jiang, M Havaei, G Chartrand, H Chouaib, T Vincent, A Jesson, ...
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
A Jesson, S Mindermann, Y Gal, U Shalit
38th International Conference on Machine Learning (ICML) 139, 4829-4838, 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
Interventions, where and how? experimental design for causal models at scale
P Tigas, Y Annadani, A Jesson, B Schölkopf, Y Gal, S Bauer
arXiv preprint arXiv:2203.02016, 2022
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
A Mehrjou, A Soleymani, A Jesson, P Notin, Y Gal, S Bauer, P Schwab
International Conference on Learning Representations (ICLR), 2022
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
A Jesson*, P Tigas*, J van Amersfoort, A Kirsch, U Shalit, Y Gal
Advances in Neural Information Processing Systems (NeurIPS) 34, 2021
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions
A Jesson, A Douglas, P Manshausen, N Meinshausen, P Stier, Y Gal, ...
arXiv preprint arXiv:2204.10022, 2022
Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific
A Jesson*, P Manshausen*, A Douglas*, D Watson-Parris, Y Gal, P Stier
Causal Inference & Machine Learning: Why now? (NeurIPS Wokshop), 2021
Adversarially learned mixture model
A Jesson, C Low-Kam, T Nair, F Soudan, F Chandelier, N Chapados
Theoretical Foundations and Applications of Deep Generative Models (ICML …, 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
McGill University (Canada), 2019
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Artikkelit 1–19