Fetching the paper…
Reading the bibliography…
Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease.
Bayesian Inference for Causal Effects: The Role of Randomization
D. B. Rubin · 1978
Earlier work this paper cites.
The central role of the propensity score in observational studies for causal effects
P. R. Rosenbaum and D. B. Rubin · 1983
Earlier work this paper cites.
On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9
J. Splawa-Neyman, D. M. Dabrowska, and T. P. Speed · 1990
Earlier work this paper cites.
Probabilistic evaluation of counterfactual queries
A. Balke and J. Pearl · 1994
Earlier work this paper cites.
A survey of medical image registration
J. A. Maintz and M. A. Viergever · 1998
Earlier work this paper cites.
Causal discovery from medical textual data
S. Mani and G. F. Cooper · 2000
Earlier work this paper cites.
Causation, prediction, and search
P. Spirtes, C. N. Glymour, R. Scheines, and D. Heckerman · 2000
Earlier work this paper cites.
Optimal structure identification with greedy search
D. M. Chickering · 2003
Earlier work this paper cites.
Causality (2nd edition)
J. Pearl · 2009
Earlier work this paper cites.
Six problems for causal inference from fmri
J. D. Ramsey, S. J. Hanson, C. Hanson, Y. O. Halchenko, R. A. Poldrack, and C. Glymour · 2010
Earlier work this paper cites.
A review of atlas-based segmentation for magnetic resonance brain images
M. Cabezas, A. Oliver, X. Lladó, J. Freixenet, and M. B. Cuadra · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Earlier work this paper cites.
Causal inference in functional magnetic resonance imaging
N. Z. Bielczyk, S. Uithol, T. van Mourik, M. N. Havenith, P. Anderson, J. C. Glennon, and K. Buitelaar · 2017
Earlier work this paper cites.
Density estimation using real nvp
L. Dinh, J. Sohl-Dickstein, and S. Bengio · 2017
Earlier work this paper cites.
Efficient multi-scale 3D CNN with fully connected crf for accurate brain lesion segmentation
K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker · 2017
Earlier work this paper cites.
Bivariate causal discovery and its applications to gene expression and imaging data analysis
R. Jiao, N. Lin, Z. Hu, D. A. Bennett, L. Jin, and M. Xiong · 2018
Earlier work this paper cites.
Causalgan: Learning causal implicit generative models with adversarial training
M. Kocaoglu, C. Snyder, A. G. Dimakis, and S. Vishwanath · 2018
Earlier work this paper cites.
Toward an understanding of adversarial examples in clinical trials
K. Papangelou, K. Sechidis, J. Weatherall, and G. Brown · 2018
Earlier work this paper cites.
Causal discovery of feedback networks with functional magnetic resonance imaging
R. Sanchez-Romero, J. Ramsey, K. Zhang, M. R. K. Glymour, B. Huang, and C. Glymour · 2018
Earlier work this paper cites.
Dags with no tears: Continuous optimization for structure learning
X. Zheng, B. Aragam, P. K. Ravikumar, and E. P. Xing · 2018
Earlier work this paper cites.
Review of causal discovery methods based on graphical models
C. Glymour, K. Zhang, and P. Spirtes · 2019
Earlier work this paper cites.
Abstract: Some investigations on robustness of deep learning in limited angle tomography
Y. Huang, T. Würfl, K. Breininger, L. Liu, G. Lauritsch, and A. Maier · 2019
Earlier work this paper cites.
A cross-center smoothness prior for variational bayesian brain tissue segmentation
W. M. Kouw, S. N. Ørting, J. Petersen, K. S. Pedersen, and M. de Bruijne · 2019
Earlier work this paper cites.
Causal discovery with attention-based convolutional neural networks
M. Nauta, D. Bucur, and C. Seifert · 2019
Earlier work this paper cites.
Identification of effective connectivity subregions
R. Sanchez-Romero, J. D. Ramsey, K. Zhang, and C. Glymour · 2019
Cited alongside, same era.
Biomechanical modelling of brain atrophy through deep learning
M. da Silva, K. Garcia, C. H. Sudre, C. Bass, M. J. Cardoso, and E. Robinson · 2020
Cited alongside, same era.
Image registration via stochastic gradient markov chain monte carlo
D. Grzech, B. Kainz, B. Glocker, and L. Le Folgoc · 2020
Cited alongside, same era.
Deep learning in medical image registration: a survey
G. Haskins, U. Kruger, and P. Yan · 2020
Cited alongside, same era.
Causal discovery from heterogeneous/nonstationary data with independent changes
B. Huang, K. Zhang, J. Zhang, J. Ramsey, R. Sanchez-Romero, C. Glymour, and B. Schölkopf · 2020
Cited alongside, same era.
The need of standardised metadata to encode causal relationships: Towards safer data-driven machine learning biological solutions
B. G. Santa Cruz, C. Vega, and F. Hertel · 2021
Later among the works it cites.
Using causal analysis for conceptual deep learning explanation
S. Singla, S. Wallace, S. Triantafillou, and K. Batmanghelich · 2021
Later among the works it cites.
Re-using adversarial mask discriminators for test-time training under distribution shifts
G. Valvano, A. Leo, and S. A. Tsaftaris · 2021
Later among the works it cites.
D’ya like dags? a survey on structure learning and causal discovery
M. J. Vowels, N. C. Camgoz, and R. Bowden · 2021
Later among the works it cites.
Harmonization with flow-based causal inference
R. Wang, P. Chaudhari, and C. Davatzikos · 2021
Later among the works it cites.
A survey on causal inference
L. Yao, Z. Chu, S. Li, Y. Li, J. Gao, and A. Zhang · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
M. Kayser, R. D. Soberanis-Mukul, A.-M. Zvereva, P. Klare, N. Navab, and S. Albarqouni · 2020
Cited alongside, same era.
Causal discovery in physical systems from videos
Y. Li, A. Torralba, A. Anandkumar, D. Fox, and A. Garg · 2020
Cited alongside, same era.
Dynotears: Structure learning from time-series data
R. Pamfil, N. Sriwattanaworachai, S. Desai, P. Pilgerstorfer, K. Georgatzis, P. Beaumont, and B. Aragam · 2020
Cited alongside, same era.
Deep structural causal models for tractable counterfactual inference
N. Pawlowski, D. C. Castro, and B. Glocker · 2020
Cited alongside, same era.
Causal inference and counterfactual prediction in machine learning for actionable healthcare
M. Prosperi, Y. Guo, M. Sperrin, J. S. Koopman, J. S. Min, X. He, S. Rich, M. Wang, I. E. Buchan, and J. Bian · 2020
Cited alongside, same era.
Clevrer: Collision events for video representation and reasoning, 2020
K. Yi, C. Gan, Y. Li, P. Kohli, J. Wu, A. Torralba, and J. B. Tenenbaum · 2020
Cited alongside, same era.
The cost of untracked diversity in brain-imaging prediction
O. Benkarim, C. Paquola, B.-y. Park, V. Kebets, S.-J. Hong, R. V. de Wael, S. Zhang, B. T. Yeo, M. Eickenberg, T. Ge, et al · 2021
Cited alongside, same era.
Later among the works it cites.
An interpretable approach to automated severity scoring in pelvic trauma
A. Zapaishchykova, D. Dreizin, Z. Li, J. Y. Wu, S. Faghihroohi, and M. Unberath · 2021
Later among the works it cites.
An empirical framework for domain generalization in clinical settings
H. Zhang, N. Dullerud, L. Seyyed-Kalantari, Q. Morris, S. Joshi, and M. Ghassemi · 2021
Later among the works it cites.
Multiple-shooting adjoint method for whole-brain dynamic causal modeling
J. Zhuang, N. Dvornek, S. Tatikonda, X. Papademetris, P. Ventola, and J. S. Duncan · 2021
Later among the works it cites.
Post hoc explanations may be ineffective for detecting unknown spurious correlation
J. Adebayo, M. Muelly, H. Abelson, and B. Kim · 2022
Closest in time.
Dbsegment: Fast and robust segmentation of deep brain structures–evaluation of transportability across acquisition domains
M. Baniasadi, M. V. Petersen, J. Goncalves, A. Horn, V. Vlasov, F. Hertel, and A. Husch · 2022
Closest in time.
Investigating underdiagnosis of ai algorithms in the presence of multiple sources of dataset bias
M. Bernhardt, C. Jones, and B. Glocker · 2022
Closest in time.
Nonlinear conditional time-varying granger causality of task fmri via deep stacking networks and adaptive convolutional kernels
K.-C. Chuang, S. Ramakrishnapillai, L. Bazzano, and O. Carmichael · 2022
Closest in time.
Neural score matching for high-dimensional causal inference
O. Clivio, F. Falck, B. Lehmann, G. Deligiannidis, and C. Holmes · 2022
Closest in time.
Carts: Causality-driven robot tool segmentation from vision and kinematics data
H. Ding, J. Zhang, P. Kazanzides, J. Y. Wu, and M. Unberath · 2022
Closest in time.
A causal framework for assessing the transportability of clinical prediction models
J. Fehr, M. Piccininni, T. Kurth, and S. Konigorski · 2022
Closest in time.
Translational lung imaging analysis through disentangled representations
P. M. Gordaliza, J. J. Vaquero, and A. Munoz-Barrutia · 2022
Closest in time.
Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
A. Holzinger, M. Dehmer, F. Emmert-Streib, R. Cucchiara, I. Augenstein, J. Del Ser, W. Samek, I. Jurisica, and N. Díaz-Rodríguez · 2022
Closest in time.
Learning to induce causal structure
N. R. Ke, S. Chiappa, J. Wang, J. Bornschein, T. Weber, A. Goyal, M. Botvinic, M. Mozer, and D. J. Rezende · 2022
Closest in time.
Counterfactual image synthesis for discovery of personalized predictive image markers
A. Kumar, A. Hu, B. Nichyporuk, J.-P. R. Falet, D. L. Arnold, S. Tsaftaris, and T. Arbel · 2022
Closest in time.
Amortized causal discovery: Learning to infer causal graphs from time-series data, 2022
S. Löwe, D. Madras, R. Zemel, and M. Welling · 2022
Closest in time.
D’artagnan: Counterfactual video generation
H. Reynaud, A. Vlontzos, M. Dombrowski, C. Lee, A. Beqiri, P. Leeson, and B. Kainz · 2022
Closest in time.
J. Schrouff, N. Harris, O. Koyejo, I. Alabdulmohsin, E. Schnider, K. Opsahl-Ong, A. Brown, S. Roy, D. Mincu, C. Chen, et al · 2022
Closest in time.
Is more data all you need? a causal exploration
A. Vlontzos, H. Reynaud, and B. Kainz · 2022
Closest in time.
Adversarial robustness through the lens of causality
Y. Zhang, M. Gong, T. Liu, G. Niu, X. Tian, B. Han, B. Schölkopf, and K. Zhang · 2022
Closest in time.