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Âé¶čAV.CA / Centre for Intelligent Machines / Research / Publications

Tal Arbel

Âé¶čAV

Headshot of Tal Arbel

Prof. Tal Arbel belongs to the Department of Electrical and Computer Engineering at Âé¶čAV and is the director of the Probabilistic Vision Group at CIM.

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2023

Q. Tian, T. Arbel and J.J. Clark, “Grow-push-prune: Aligning deep discriminants for effective structural network compression”, Journal of Computer Vision and Image Understanding, Volume 231, p. 103682, June 2023.

J. Durso-Finley, J.P. Falet, R. Mehta, D.L. Arnold, N. Pawlowski, and T. Arbel, “Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models”, In Proceedings of the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), held in Vancouver, October 8-12, 2023. Shortlisted for Best Paper Award/Young Scientist Award.

C. Shui, J. Szeto, R. Mehta, D.L. Arnold, and T. Arbel, “Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis”, In Proceedings of the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), held in Vancouver, October 8-12, 2023.

A. Kumar, N. Fathi, R. Mehta, B. Nichyporuk, J.P. Falet, S. Tsaftaris, and T. Arbel, “Debiasing Counterfactuals In the Presence of Spurious Correlations“, Fairness of AI in Medical Imaging Workshop, held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), Vancouver, October 8-12, 2023. Best Oral Presentation Award.

R. Mehta, C. Shui, T. Arbel, “Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis”, In Proceedings of the 6th Conference on Medical Imaging with Deep Learning (MIDL 2023), held in Nashville, USA, July 10-12, 2023.

2022

Brennan Nichyporuk et al. “Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation”. In: The Journal of Machine Learning for Biomedical Imaging (2022).

Jean-Pierre R Falet et al. “Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning”. In: Nature Communications 13.1 (2022), pp. 1–12.

Raghav Mehta et al. “QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation-Analysis of Ranking Scores and Benchmarking Results”. In: Journal of Machine Learning for Biomedical Imaging 1 (2022).

Raghav Mehta et al. “Propagating uncertainty across cascaded medical imaging tasks for improved deep learning inference”. In: IEEE Transactions on Medical Imaging 41.2 (2021), pp. 360–373.

Changjian Shui et al. “On learning fairness and accuracy on multiple subgroups”. In: Advances in Neural Information Processing Systems 35 (2022), pp. 34121–34135.

Raghav Mehta et al. “Information Gain Sampling for Active Learning in Medical Image Classification”. In: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: 4th International Workshop, UNSURE 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings. 2022, pp. 135–145.

Chelsea Myers-Colet et al. “Heatmap Regression for Lesion Detection Using Pointwise Annotations”. In: Medical Image Learning with Limited and Noisy Data: First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. 2022, pp. 3–12.

Amar Kumar et al. “Counterfactual image synthesis for discovery of personalized predictive image markers”. In: Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery: First MICCAI Workshop, AIIIMA 2022, and First MICCAI Workshop, MIABID 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings. 2022, pp. 113–124.

Joshua Durso-Finley et al. “Personalized prediction of future lesion activity and treatment effect in multiple sclerosis from baseline MRI”. In: International Conference on Medical Imaging with Deep Learning. 2022, pp. 387–406.

Julien Schroeter et al. “Segmentation- Consistent Probabilistic Lesion Counting”. In: International Conference on Medical Imaging with Deep Learning. 2022, pp. 1034–1056.

Anjun Hu et al. “Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors”. In: Machine Learning for Health Symposium (ML4H 2022) (2022).

Anjun Hu et al. “Structured Priors for Disentangling Pathology and Anatomy in Patient Brain MRI”. In: Medical Imaging Meets NeurIpS (2022).

Annika Reinke et al. “Metrics Reloaded-A new recommendation framework for biomedical image analysis validation”. In: Medical Imaging with Deep Learning. 2022.

2021

Q. Tian, T. Arbel, and J. J. Clark. “Task dependent Deep LDA pruning of neural networks,” Journal of Computer Vision and Image Understanding, Volume 203, p. 103154, February 2021.

B. Nichyporuk. J. Cardinell, J. Szeto, R. Mehta, D.L. Arnold, S.Tsaftaris, and T. Arbel. “Cohort Bias Adaptation in Federated Datasets for Lesion Segmentation,” In Proceedings of the 3rd MICCAI Workshop on Domain Adaptation and Representation Transfer (DART) held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), held virtually (Strasbourg, France), September 2021. Lecture Notes in Computer Science, Springer, Vol. 12968, pp. 101-111, 2021. Best Paper Award.

S. Vadacchino, R. Mehta, N. Mohammadi- Sepahvand, B. Nichyporuck, J.J. Clark, and T. Arbel. “HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images,” in Proceedings of the 4th Conference on Medical Imaging with Deep Learning (MIDL 2021), held virtually (Lubeck, Germany), July 7-9, 2021. Proceedings of Machine Learning Research (PLMR), pp. 787-801.

X. Bouthillier, P. Delaunay, M. Bronzi, A. Trofimov, B. Nichyporuk, J. Szeto, N. Mohammadi Sepahvand, E. Raff, K. Madan, V. Voleti, S. Ebrahimi Kahou, V. Michalski, T. Arbel, C. Pal, G. Varoquaux, and P. Vincent. “Accounting for Variance in Machine Learning Benchmarks,” in Proceedings of the 4th Conference on Machine Learning and Systems (MLSys 2021), held virtually, April 5-9, 2021.

B. Nichyporuk, J. Szeto, D.L. Arnold, and T. Arbel. “Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting,” the 4th Conference on Medical Imaging with Deep Learning (MIDL 2021), held virtually (Lubeck, Germany), July 7-9, 2021. (short paper)

A. Reinke, M. Eisenmann, M. Dietlinde Tizabi, C. H. Sudre, T. RÀdsch, M. Antonelli, T. Arbel, S. Bakas, M. J. Cardoso, V. Cheplygina, K. Farahani, B. Glocker, D. Heckmann-NÃtzel, F. Isensee, P. Jannin, C. E. Kahn, J. Kleesiek, T. Kurc, M. Kozubek, B. A. Landman, G. Litjens, K. Maier-Hein, B. Menze, H. MÃller, J. Petersen, M. Reyes, N. Rieke, B. Stieltjes, R. M. Summers, S. A. Tsaftaris, B. van Ginneken, A. Kopp- Schneider, P. JÃger, and L. Maier-Hein. “Common limitations of performance metrics in biomedical image analysis,” 4th Conference on Medical Imaging with Deep Learning (MIDL 2021), held virtually (Lubeck, Germany), July 7-9, 2021. (short paper - Audience Award for Best Short Oral Presentation)

2020

L. Maier-Hein, A. Reinke, M. Kozubek, A. L. Martel, T. Arbel, M. Eisenmann, A. Hanbury, P. Jannin, H. Muller, S. Onogur, J. Saez-Rodriguez, B. van Ginneken, A. Kopp-Schneider and B. A. Landman, "BIAS: Transparent reporting of biomedical image analysis challenges", Medical Image Analysis, Volume 66, pp. 101796, August 2020.

T. Nair, D. Precup, D.L. Arnold and T. Arbel, "Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation", Medical Image Analysis, MICCAI 2018 Special Issue, Volume 59, January 2020.

N. Mohammadi-Sepahvand, D.L. Arnold and T. Arbel, "CNN Detection of New and Enlarging Multiple Sclerosis Lesions from Longitudinal MRI using Subtraction Images", the IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020), Iowa City, USA, April 2020, pp. 27-130.

R. Mehta, A. Filos, Y. Gal, T. Arbel, “Uncertainty Evaluation Metrics for Brain Tumour Segmentation”, Medical Imaging with Deep Learning (MIDL) 2020.

2019

N. K. Subbanna, D. Rajashekar, B. Cheng, G. Thomalla, J. Fiehler, T. Arbel and N. D. Forkert, "Stroke Lesion Segmentation in FLAIR MRI Datasets Using Customized Markov Random Fields", Frontiers in Neurology, Section Stroke, May 2019.

R. Mehta, T. Christinck, T. Nair, P. Lemaitre, D.L. Arnold and T. Arbel, "Propagating Uncertainty Across Cascaded Medical Imaging Tasks For Improved Deep Learning Inference", in Proceedings of UNSURE 2019: First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, October 2019 (oral presentation: 1 of 3 papers accepted for oral presentation) BEST PAPER AWARD.

Kaur, P. Lemaitre, R. Mehta, N. Mohammadi-Sepahvand, D. Precup, D.L. Arnold and T. Arbel, "Improving Pathological Structure Segmentation Via Transfer Learning Across Diseases", in Proceedings of DART 2019: First International Workshop on Domain Adaptation and Representation Transfer, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, October 2019.

A. Tousignant, D. Precup, D.L. Arnold and T. Arbel, "Prediction of Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data", in Proceedings of the 2nd International Conference on Medical Imaging with Deep Learning (MIDL 2019), London, U.K., July 2019. Proceedings of Machine Learning Research, Volume 120, pp. 483-492.

J. Durso-Finley, D.L. Arnold and T. Arbel, "Saliency Based Deep Neural Network for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI", in MICCAI Brain-Lesion (Brain-Les) Workshop 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, October 2019.

2018

Q. Tian, T. Arbel and J. J. Clark, “Structured Deep Fisher Pruning for Efficient Facial Trait Classification”, Image and Vision Computing, Spe-cial issue on Biometric in the Wild, Vol. 77, pp. 45-59, September 2018.

I. Gerard, M. Kersten-Oertel, S. Drouin, J. Hall, K. Petrecca, D. De Nigris Moreno, D. Di Giovanni, T. Arbel, D.L. Collins, “Combining intra-operative ultrasound brain shift correction and augmented reality visualizations: a pilot study of 8 cases”, Journal of Medical Imaging, Vol. 2, Issue 2, January 2018.

T. Nair, D. Precup, D.L. Arnold and T. Arbel, “Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation”, in Proceedings of the 21st International Confer-ence on Medical Image Computing and Computer Assisted Interven-tion (MICCAI 2018), Grenada, Spain, September 2018. Lecture Notes in Computer Science, Springer, Vol. 11070, pp. 655-663.

A. Reinke, M. Eisenmann, S. Onogur, M. Stankovic, P. Scholz, P.M. Full, H. Bogunovic, B.A. Landman, O. Maier, B. Menze, G. Sharp, K. Si-rinukunwattana, S. Speidel, F. van der Sommen, G. Zheng, H. Muller, M. Kozubek, T. Arbel, A.P. Bradley, P. Jannin, A. Kopp-Schneider and L. Maier-Hein, “How to Exploit Weaknesses in Biomedical Design and Organization?”, in Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018), Grenada, Spain, September 2018. Lecture Notes in Computer Science, Springer, Vol. 11073, pp. 388-395.

R. Mehta and T. Arbel, “RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours”, in Proceedings of the Workshop SASHIMI 2018: Simulation and Synthesis in Medical Imaging, held in conjunction with the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018), Grenada, Spain, September 2018. Lecture Notes in Computer Science, Springer, Vol. 11037, pp. 119-129.

N. Mohammadi-Sepahvand, T. Hassner, D.L. Arnold and T. Arbel, “CNN Prediction of Future Disease Activity for Multiple Sclerosis Pa-tients from Baseline MRI and Lesion Labels”, in Proceedings of the Brain Lesions (Brainles) Workshop, held in conjunction with the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018), Grenada, Spain, September 2018.

A. Tousignant, D. Precup, T. Arbel, “Prediction of Progression in Multi-ple Sclerosis Patients”, Workshop on Medical Imaging Meets NeurIPS held in conjunction with the 32nd Conference on Neural Information Processing Systems, Montreal, Quebec, Canada, December 2018.

R. Mehta, T. Arbel, “RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tu-mour”, Workshop on Medical Imaging Meets NeurIPS held in conjunc-tion with the 32nd Conference on Neural Information Processing Sys-tems, Montreal, Quebec, Canada, December 2018.

R. Mehta and T. Arbel, “3D U-Net for brain tumour segmentation”, in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Crimi A., Bakas S., Kuijf H., Keyvan F., Reyes M., Van Walsum T.(eds), Proceedings of International MICCAI Multimodal Brain Tu-mour Segmentation Challenge 2018 held in conjunction with the 21th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018) , Granada, Spain, September 2018. Lecture Notes in Computer Science, Springer, Vol. 11384, pp. 254-266

2017

A. Carass, S. Roy, A. Jog, J. L. Cuzzocreo, E. Magrath, A. Gherman, J. Button, J. Nguyen, F. Prados, C. H. Sudre, M. J. Cardoso, N. Cawley, O. Ciccarelli, C. A. M. Wheeler-Kingshott, S. Ourselin, L. Catanese, H. Deshpande, P. Maurel, O. Commowick, C. Barillot, X. Tomas-Fernandez, S. K. Warfield, S. Vaidya, A. Chunduru, R. Muthuganapathy, G. Krishnamurthi, A. Jesson, T. Arbel, O. Maier, H. Handels, L. O. Iheme, D. Unay, S. Jain, D. M. Sima, D. Smeets, M. Ghafoorian, B. Platel, A. Birenbaum, H. Greenspan, P. L. Bazin, P. A. Calabresi, C. M. Crainiceanu, L. M. Ellingsen, D. S. Reich, J. L. Prince, D. L. Pham, "Longitudinal Multiple Sclerosis Lesion Segmentation: Resource & Challenge", NeuroImage, 148, pp. 77-102, January 2017.

A. Doyle, D. Precup, D.L. Arnold, T. Arbel, "Predicting Future Disease Activity and Treatment Responders for Multiple Sclerosis Patients using a Bag-of-Lesions Brain Representation", in Proceedings of the 20th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2017), Quebec City, Quebec, Canada, September 2017, Lecture Notes in Computer Science, Springer, Vol. 10435, pp. 186-194.

Q. Tian, T. Arbel, J.J. Clark, "Deep LDA-Pruned Nets for Efficient Facial Gender Classification", in Proceedings of the IEEE Computer Society Workshop on Biometrics held in conjunction with the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, Hawaii, U.S.A., July 2017.

A. Jesson and T. Arbel, "Brain Tumour Segmentation Using a 3D FCN with Multi-Scale Loss", in Proceedings of the "BRaTS Multimodal Brain Tumour Segmentation Challenge", held in conjunction with the 20th International Conference on Medical Image Computing and Computer-Assisted Invervention (MICCAI 2017), Quebec City, Quebec, Canada, September 2017.

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