Strategie: Statistically Efficient Structured Prediction for Computer Vision and Medical Imaging

Matthew B. Blaschko

Overview:

This project involves the development of statistical methodologies for structured prediction and their application to computer vision and medical image analysis.

This page collects publications, datasets, and other results related to our research produced with the support of the "Strategie" Marie Curie Career Integration Grant with PI Matthew Blaschko.

• Eugene Belilovsky
• Maxim Berman
• Amal Rannen Triki

1. Belilovsky, E.: Structured Sparse Learning on Graphs in High-Dimensional Data with Applications to Neuroimaging. PhD Thesis, Université Paris-Saclay & KU Leuven, 2018
    
2. Bounliphone, W.: Statistically and computationally efficient hypothesis tests for dependency and similarity. PhD Thesis, Université Paris-Saclay & KU Leuven, 2017
    
3. Yu, J.: Empirical risk minimization with non-modular loss functions. PhD Thesis, Université Paris-Saclay, 2017
    
4. Orlando, J. I.: Machine learning for ophthalmic screening and diagnostics from fundus images. PhD Thesis, Universidad Nacional del Centro de la Provincia de Buenos Aires, 2017.
    
5. Rannen Triki, A., M. Berman, and M. B. Blaschko: Stochastic Weighted Function Norm Regularization. arXiv:1710.06703, 2017. [bibtex]
    
6. Rannen Triki, A., R. Aljundi, M. B. Blaschko, and T. Tuytelaars: Encoder Based Lifelong Learning. International Conference on Computer Vision (ICCV), 2017. [bibtex; doi]
    
7. Orlando, J. I., K. van Keer, J. Barbosa Breda, H. L. Manterola, M. B. Blaschko, and A. Clausse: Proliferative Diabetic Retinopathy Characterization based on Fractal Features: Evaluation on a Publicly Available Data Set. Medical Physics, 44(12):6425-6434, 2017. [bibtex; code; doi]
    
8. Blaschko, M. B.: Slack and Margin Rescaling as Convex Extensions of Supermodular Functions. International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2017. [bibtex; code]
    
9. Berman, M. and M. B. Blaschko: Optimization of the Jaccard index for image segmentation with the Lovász hinge. arXiv:1705.08790, 2017. [bibtex; code]
    
10. Belilovsky, E., K. Kastner, G. Varoquaux, and M. B. Blaschko: Learning to Discover Sparse Graphical Models. International Conference on Machine Learning (ICML), 2017. [bibtex; code; video]
     
11. Rannen Triki, A., M. B. Blaschko, Y. M. Jung, S. Song, H. J. Han, S. I. Kim, C. Joo: Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks. arXiv:1703.10827, 2017. [bibtex; code]
     
12. Belilovsky, E., M. B. Blaschko, J. R. Kiros, R. Urtasun, R. Zemel: Joint Embeddings of Scene Graphs and Images. International Conference on Learning Representations Workshop Track (ICLR), 2017. [bibtex]
     
13. Belilovsky, E., K. Kastner, G. Varoquaux, and M. B. Blaschko: Learning to Discover Sparse Graphical Models. International Conference on Learning Representations Workshop Track (ICLR), 2017. [bibtex; code]
     
14. Yu, J. and M. B. Blaschko: An Efficient Decomposition Framework for Discriminative Segmentation with Supermodular Losses. arXiv:1702.03690, 2017. [bibtex; code]
     
15. Orlando, J. I., E. Prokofyeva, and M. B. Blaschko: A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images. IEEE Transactions on Biomedical Engineering, 64(1):16-27, 2017. [bibtex; code and data; doi]
     
16. Belilovsky, E., G. Varoquaux, and M. B. Blaschko: Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity. Neural Information Processing Systems (NIPS), 2016. [bibtex; code]
     
17. Rannen Triki, A. and M. B. Blaschko: Stochastic Function Norm Regularization of DNNs. NIPS Workshop on Optimization for Machine Learning (OPT), 2016. [bibtex; code]
     
18. Berman, M. and M. B. Blaschko: Efficient optimization for probably submodular constraints in CRFs. NIPS Workshop on Constructive Machine Learning, 2016. [bibtex; code]
     
19. Rannen Triki, A. and M. B. Blaschko: Stochastic Function Norm Regularization of Deep Networks. In Women in Machine Learning Workshop. Barcelona, Spain, 2016. [bibtex; code]
     
20. Yu, J. and M. B. Blaschko: Efficient Learning for Discriminative Segmentation with Supermodular Losses. In Women in Machine Learning Workshop. Barcelona, Spain, 2016. [bibtex; code]
     
21. Bounliphone, W., E. Belilovsky, A. Tenenhaus, I. Antonoglou, A. Gretton, and M. B. Blaschko: Fast Non-Parametric Tests of Relative Dependency and Similarity. arXiv:1611.05740, 2016. [bibtex; code; code]
     
22. Orlando, J. I., E. Prokofyeva, M. del Fresno, and M. B. Blaschko: Convolutional Neural Network Transfer for Automated Glaucoma Identification. International Symposium on Medical Information Processing and Analysis, 2016. [bibtex; code; doi]
     
23. Yu, J. and M. B. Blaschko: Efficient Learning for Discriminative Segmentation with Supermodular Losses. British Machine Vision Conference (BMVC), 2016. [bibtex; code; doi]
     
24. Yu, J. and M. B. Blaschko: A Convex Surrogate Operator for General Non-Modular Loss Functions. Benelearn. Kortrijk, Belgium, 2016. [bibtex; code]
     
25. Rannen Triki, A. and M. B. Blaschko: Stochastic Function Norm Regularization of Deep Networks. arXiv:1605.09085, 2016. [bibtex; code]
     
26. Bounliphone, W. and M. B. Blaschko: A U-statistic Approach to Hypothesis Testing for Structure Discovery in Undirected Graphical Models. arXiv:1604.01733, 2016. [bibtex; code]
     
27. Ghafarianzadeh, M., M. B. Blaschko, and G. Sibley: Efficient, Dense, Object-based Segmentation from RGBD Video. IEEE International Conference on Robotics and Automation (ICRA), 2016. [bibtex; doi]
     
28. Yu, J. and M. B. Blaschko: A Convex Surrogate Operator for General Non-Modular Loss Functions. International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. [bibtex; code]
     
29. Zaremba, W. and M. B. Blaschko: Discriminative training of CRF models with probably submodular constraints. IEEE Winter Conference on Applications of Computer Vision (WACV), 2016. [bibtex; code; doi]
     
30. Bounliphone, W., E. Belilovsky, M. B. Blaschko, I. Antonoglou, and A. Gretton: A Test of Relative Similarity for Model Selection in Generative Models. International Conference on Learning Representations (ICLR), 2016. [Note: Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award; bibtex; code]
     
31. Gkirtzou, K. and M. B. Blaschko: The Pyramid Quantized Weisfeiler-Lehman Graph Representation. Neurocomputing, 173(3):1495-1507, 2016. [bibtex; code; doi]
     
32. Sidahmed, H., E. Prokofyeva, and M. B. Blaschko: Discovering Predictors of Mental Health Service Utilization with k-support Regularized Logistic Regression. Information Sciences, 329:937-949, 2016. [bibtex; code; doi]
     
33. Yu, J. and M. B. Blaschko: The Lovász Hinge: A Convex Surrogate for Submodular Losses. arXiv:1512.07797, 2015. [bibtex; code]
     
34. Blaschko, M. B. and J. Yu: Hardness Results for Structured Learning and Inference with Multiple Correct Outputs. Constructive Machine Learning Workshop at ICML, 2015. [bibtex]
     
35. Belilovsky, E., A. Argyriou, G. Varoquaux, and M. B. Blaschko: Convex Relaxations of Penalties for Sparse Correlated Variables With Bounded Total Variation. Machine Learning, 100(2-3):533-553, 2015. [bibtex; code; doi]
     
36. Yu, J. and M. B. Blaschko: Learning Submodular Losses with the Lovász Hinge. International Conference on Machine Learning (ICML), 2015. [bibtex; code]
     
37. Bounliphone, W., A. Gretton, A. Tenenhaus, and M. B. Blaschko: A low variance consistent test of relative dependency. International Conference on Machine Learning (ICML), 2015. [bibtex; code]
     
38. Belilovsky, E., K. Gkirtzou, M. Misyrlis, A. B. Konova, J. Honorio, N. Alia-Klein, R. Z. Goldstein, D. Samaras, and M. B. Blaschko: Predictive sparse modeling of fMRI data for improved classication, regression, and visualization using the k-support norm. Computerized Medical Imaging and Graphics, 46(1):40-46, 2015. [bibtex; code; doi]
     
39. Belilovsky, E., A. Argyriou, and M. B. Blaschko: Approximating Combined Discrete Total Variation and Correlated Sparsity With Convex Relaxations. NIPS Workshop on Discrete and Combinatorial Problems in Machine Learning, 2014. [bibtex; code]
     
40. Bounliphone, W., A. Gretton, and M. B. Blaschko: Kernel non-parametric tests of relative dependency. NIPS Workshop on Modern Nonparametrics 3: Automating the Learning Pipeline, 2014. [bibtex; code]
     
41. Yu, J. and M. B. Blaschko: Lovasz Hinge for Learning Submodular Losses. NIPS Workshop on Representation and Learning Methods for Complex Outputs, 2014. [bibtex; code]
     
42. Blaschko, M. B.: Advances in Empirical Risk Minimization for Image Analysis and Pattern Recognition. Mémoire d'habilitation à diriger des recherches, École Normale Supérieure de Cachan, 2014. [bibtex]
     
43. Orlando, J. I. and M. B. Blaschko: Learning fully-connected CRFs for blood vessel segmentation in retinal images. Medical Image Computing and Computer Assisted Intervention (MICCAI), 2014. [bibtex; data; doi]
     
44. Ghafarianzadeh, M., M. B. Blaschko, and G. Sibley: Unsupervised Spatio-Temporal Segmentation with Sparse Spectral-Clustering. British Machine Vision Conference (BMVC), 2014. [bibtex; doi]
     
45. Blaschko, M. B., A. Mittal, and E. Rahtu: An O(n log n) Cutting Plane Algorithm for Structured Output Ranking. 36th German Conference on Pattern Recognition (GCPR), 2014. [bibtex; doi]
     
46. Misyrlis, M., A. B. Konova, M. B. Blaschko, J. Honorio, N. Alia-Klein, R. Z. Goldstein, D. Samaras: Predicting cross-task behavioral variables from fMRI data using the k-support norm. Sparsity Techniques in Medical Imaging, 2014. [Note: Best paper award; bibtex; code]
     
47. Vedaldi, A., S. Mahendran, S. Tsogkas, S. Maji, R. Girshick, J. Kannala, E. Rahtu, I. Kokkinos, M. B. Blaschko, D. Weiss, B. Taskar, K. Simonyan, N. Saphra, and S. Mohamed: Understanding Objects in Detail with Fine-grained Attributes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. [bibtex; code; data; doi]
     
48. Blaschko, M. B.: Machine Learning for Neurological Disorders. Centraliens, 632 (2014) 40-42. [bibtex]
     

Acknowledgements:

We acknowledge support from Marie Skłodowska-Curie actions through project number FP7-MC-CIG 334380.