Publications

Development and Validation of Multiparametric MRI-based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer
Thierry L. Lefebvre
Yoshiko Ueno
Anthony Dohan
Avishek Chatterjee
Martin Vallières
Eric Winter-Reinhold
Sameh Saif
Ives R. Levesque
Xing Ziggy Zeng
Reza Forghani
Jan Seuntjens
Philippe Soyer
Peter Savadjiev
Caroline Reinhold
Background Stratifying high-risk histopathologic features in endometrial carcinoma is important for treatment planning. Radiomics analysis a… (see more)t preoperative MRI holds potential to identify high-risk phenotypes. Purpose To evaluate the performance of multiparametric MRI three-dimensional radiomics-based machine learning models for differentiating low- from high-risk histopathologic markers-deep myometrial invasion (MI), lymphovascular space invasion (LVSI), and high-grade status-and advanced-stage endometrial carcinoma. Materials and Methods This dual-center retrospective study included women with histologically proven endometrial carcinoma who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. Exclusion criteria were tumor diameter less than 1 cm, missing MRI sequences or histopathology reports, neoadjuvant therapy, and malignant neoplasms other than endometrial carcinoma. Three-dimensional radiomics features were extracted after tumor segmentation at MRI (T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI). Predictive features were selected in the training set with use of random forest (RF) models for each end point, and trained RF models were applied to the external test set. Five board-certified radiologists conducted MRI-based staging and deep MI assessment in the training set. Areas under the receiver operating characteristic curve (AUCs) were reported with balanced accuracies, and radiologists' readings were compared with radiomics with use of McNemar tests. Results In total, 157 women were included: 94 at the first institution (training set; mean age, 66 years ± 11 [SD]) and 63 at the second institution (test set; 67 years ± 12). RF models dichotomizing deep MI, LVSI, high grade, and International Federation of Gynecology and Obstetrics (FIGO) stage led to AUCs of 0.81 (95% CI: 0.68, 0.88), 0.80 (95% CI: 0.67, 0.93), 0.74 (95% CI: 0.61, 0.86), and 0.84 (95% CI: 0.72, 0.92), respectively, in the test set. In the training set, radiomics provided increased performance compared with radiologists' readings for identifying deep MI (balanced accuracy, 86% vs 79%; P = .03), while no evidence of a difference was observed in performance for advanced FIGO stage (80% vs 78%; P = .27). Conclusion Three-dimensional radiomics can stratify patients by using preoperative MRI according to high-risk histopathologic end points in endometrial carcinoma and provide nonsignificantly different or higher performance than radiologists in identifying advanced stage and deep myometrial invasion, respectively. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kido and Nishio in this issue.
2022-10-31
Radiology (published)
doi.org
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning
Riashat Islam
Hongyu Zang
Anirudh Goyal
Alex Lamb
Kenji Kawaguchi
Xin Li
Romain Laroche
Yoshua Bengio
Remi Tachet des Combes
2022-10-31
arXiv (preprint)
doi.org
arxiv.org
Successive-Cancellation Decoding of Reed-Muller Codes With Fast Hadamard Transform
Nghia Doan
Seyyed Ali Hashemi
Warren J. Gross
A novel permuted fast successive-cancellation list decoding algorithm with fast Hadamard transform (FHT-FSCL) is presented. The proposed dec… (see more)oder initializes
2022-10-31
IEEE Transactions on Vehicular Technology (published)
doi.org
arxiv.org
Type B Ultra Long-Range Interactions in PFAS (TULIPs) Are Recurrent Epigenomic Features of PFA Ependymoma
Michael Johnston
John JY Lee
Bo Hu
Ana Nikolic
Audrey Baguette
Seungil Paik
Haifen Chen
Sachin Kumar
Carol Chen
Selin Jessa
Polina Balin
Vernon Fong
Melissa Zwaig
Kulandaimanuvel MichealRaj
Xun Chen
Yanlin Zhang
Srinidhi Varadharajan
Pierre Billon
Nikoleta Juretic
Craig Daniels … (see 21 more)
Caterina Giannini
Eric Thompson
Peter Hauser
Seung-Ki Kim
Kyu-Chang Wang
Ji Yeoun Lee
Wieslawa Grajkowska
Sameer Agnihotri
Stephen C. Mack
Benjamin Ellezam
Alex Weil
Guillaume Bourque
Jennifer Chan
Mathieu Lupien
Jiannis Ragoussis
Claudia Kleinman
Jacek Majewski
Nada Jabado
Michael Taylor
Mathieu Blanchette
Marco Gallo
Posterior Fossa Group A (PFA) ependymomas are pediatric brain tumors with extremely poor survival outcomes. As protein-coding mutations in P… (see more)FA are exceedingly rare, the underlying etiology of these tumors remains elusive. Elevated CpG island methylation and depletion of H3K27me3 have been described in PFA, leading to the hypothesis that PFA may be driven by a dysregulated epigenetic state. In this study, we sought to determine how three-dimensional (3D) genome features (such as DNA loops, domains, and compartments) differ between pediatric brain tumors. We performed Hi-C sequencing on a collection of 64 patient specimens and patient-derived primary cultures that collectively span multiple subgroups of ependymoma, medulloblastoma, high-grade glioma, and non-neoplastic brain. For certain samples, we further performed RNA-seq, histone modification ChIP-seq, or whole-genome bisulfite sequencing to allow multiomic data integration. Overall, the 3D genome organization of PFA samples appeared distinct from other tumor types. We identified and defined TULIPs: a subset of type B compartments, separated by genomic distances greater than 10 Mbp, that exhibit a striking fivefold increase in reciprocal interaction strength. These TULIPs recurred at the same genomic positions across the vast majority of PFA samples with minimal representation among other tumor or non-tumor samples. TULIPs displayed enrichment for heterochromatic features such as H3K9me3 and late replication timing and were depleted of euchromatic features such as H3K27ac and protein-coding genes. By using immuno-fluorescence for H3K9me3 and oligo-FISH to label TULIP regions, we demonstrated that TULIP regions are more compact in PFA than other tumors. Finally, by applying inhibitors of H3K9 lysine methylation to PFA cultures we showed that TULIPs become more diffuse and cell viability is reduced. Altogether, this work defines TULIPs as highly recurrent epigenetic features of PFA tumors.
2022-10-31
Neuro-Oncology (published)
doi.org
Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise
Eduard Gorbunov
Marina Danilova
David Dobre
Pavel Dvurechensky
Alexander Gasnikov
Gauthier Gidel
2022-10-30
NeurIPS.cc/2022/Conference (accept)
doi.org
openreview.net
Flexible Diffusion Modeling of Long Videos
William Harvey
Saeid Naderiparizi
Vaden Masrani
Christian Dietrich Weilbach
Frank N. Wood
We present a framework for video modeling based on denoising diffusion probabilistic models that produces long-duration video completions in… (see more) a variety of realistic environments. We introduce a generative model that can at test-time sample any arbitrary subset of video frames conditioned on any other subset and present an architecture adapted for this purpose. Doing so allows us to efficiently compare and optimize a variety of schedules for the order in which frames in a long video are sampled and use selective sparse and long-range conditioning on previously sampled frames. We demonstrate improved video modeling over prior work on a number of datasets and sample temporally coherent videos over 25 minutes in length. We additionally release a new video modeling dataset and semantically meaningful metrics based on videos generated in the CARLA autonomous driving simulator.
2022-10-30
NeurIPS.cc/2022/Conference (accept)
doi.org
openreview.net
Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis
Iris Ganser
Rodolphe Thiébaut
David L Buckeridge
2022-10-30
JMIR Public Health and Surveillance (published)
doi.org
On Learning Fairness and Accuracy on Multiple Subgroups
Changjian Shui
Gezheng Xu
Qi CHEN
Jiaqi Li
Charles Ling
Tal Arbel
Boyu Wang
Christian Gagné
We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of g… (see more)roup sufficiency. We focus on the scenario where the data contains multiple or even many subgroups, each with limited number of samples. As a result, we present a principled method for learning a fair predictor for all subgroups via formulating it as a bilevel objective. Specifically, the subgroup specific predictors are learned in the lower-level through a small amount of data and the fair predictor. In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors. We further prove that such a bilevel objective can effectively control the group sufficiency and generalization error. We evaluate the proposed framework on real-world datasets. Empirical evidence suggests the consistently improved fair predictions, as well as the comparable accuracy to the baselines.
2022-10-30
NeurIPS.cc/2022/Conference (accept)
doi.org
openreview.net
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
Rishabh Agarwal
Marc Bellemare
Pablo Samuel Castro
Aaron Courville
Marc Bellemare
Max Schwarzer
2022-10-30
Neural Information Processing Systems (accepted)
doi.org
openreview.net
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning
Setareh Cohan
Nam Hee Gordon Kim
David Rolnick
Michiel van de Panne
Policies produced by deep reinforcement learning are typically characterised by their learning curves, but they remain poorly understood in … (see more)many other respects. ReLU-based policies result in a partitioning of the input space into piecewise linear regions. We seek to understand how observed region counts and their densities evolve during deep reinforcement learning using empirical results that span a range of continuous control tasks and policy network dimensions. Intuitively, we may expect that during training, the region density increases in the areas that are frequently visited by the policy, thereby affording fine-grained control. We use recent theoretical and empirical results for the linear regions induced by neural networks in supervised learning settings for grounding and comparison of our results. Empirically, we find that the region density increases only moderately throughout training, as measured along fixed trajectories coming from the final policy. However, the trajectories themselves also increase in length during training, and thus the region densities decrease as seen from the perspective of the current trajectory. Our findings suggest that the complexity of deep reinforcement learning policies does not principally emerge from a significant growth in the complexity of functions observed on-and-around trajectories of the policy.
2022-10-30
NeurIPS.cc/2022/Conference (accept)
doi.org
openreview.net
Washing The Unwashable : On The (Im)possibility of Fairwashing Detection
Ali Shahin Shamsabadi
Mohammad Yaghini
Natalie Dullerud
Sierra Wyllie
Ulrich Matchi Aïvodji
Aisha Alaagib Alryeh Mkean
Sébastien Gambs
Nicolas Papernot
The use of black-box models (e.g., deep neural networks) in high-stakes decision-making systems, whose internal logic is complex, raises the… (see more) need for providing explanations about their decisions. Model explanation techniques mitigate this problem by generating an interpretable and high-fidelity surrogate model (e.g., a logistic regressor or decision tree) to explain the logic of black-box models. In this work, we investigate the issue of fairwashing, in which model explanation techniques are manipulated to rationalize decisions taken by an unfair black-box model using deceptive surrogate models. More precisely, we theoretically characterize and analyze fairwashing, proving that this phenomenon is difficult to avoid due to an irreducible factor---the unfairness of the black-box model. Based on the theory developed, we propose a novel technique, called FRAUD-Detect (FaiRness AUDit Detection), to detect fairwashed models by measuring a divergence over subpopulation-wise fidelity measures of the interpretable model. We empirically demonstrate that this divergence is significantly larger in purposefully fairwashed interpretable models than in honest ones. Furthermore, we show that our detector is robust to an informed adversary trying to bypass our detector. The code implementing FRAUD-Detect is available at https://github.com/cleverhans-lab/FRAUD-Detect.
2022-10-30
NeurIPS.cc/2022/Conference (accepted)
openreview.net
openreview.net
Behind the Machine's Gaze: Neural Networks with Biologically-inspired Constraints Exhibit Human-like Visual Attention
Leo Schwinn
Doina Precup
Bjoern Eskofier
Dario Zanca
By and large, existing computational models of visual attention tacitly assume perfect vision and full access to the stimulus and thereby de… (see more)viate from foveated biological vision. Moreover, modeling top-down attention is generally reduced to the integration of semantic features without incorporating the signal of a high-level visual tasks that have been shown to partially guide human attention. We propose the Neural Visual Attention (NeVA) algorithm to generate visual scanpaths in a top-down manner. With our method, we explore the ability of neural networks on which we impose a biologically-inspired foveated vision constraint to generate human-like scanpaths without directly training for this objective. The loss of a neural network performing a downstream visual task (i.e., classification or reconstruction) flexibly provides top-down guidance to the scanpath. Extensive experiments show that our method outperforms state-of-the-art unsupervised human attention models in terms of similarity to human scanpaths. Additionally, the flexibility of the framework allows to quantitatively investigate the role of different tasks in the generated visual behaviors. Finally, we demonstrate the superiority of the approach in a novel experiment that investigates the utility of scanpaths in real-world applications, where imperfect viewing conditions are given.
2022-10-29
TMLR (accepted)
openreview.net
openreview.net