Publications

Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT
Vincent Andrearczyk
Valentin Oreiller
Moamen A. Abobakr
Azadeh Akhavanallaf
Panagiotis Balermpas
Sarah Boughdad
Leo Capriotti
Joel Castelli
Catherine Cheze Le Rest
Pierre Decazes
Ricardo Correia
D. El-Habashy
Hesham M. Elhalawani
C. Fuller
Mario Jreige
Yomna Khamis
Agustina La Greca Saint-Esteven
Aliae Ar Hussein Mohamed
M. Naser
John O. Prior … (see 11 more)
Su Ruan
Stephanie Tanadini-Lang
Olena Tankyevych
Yazdan Salimi
Martin Vallières
Pierre Véra
Dimitris Visvikis
K. Wahid
Habib Zaidi
Mathieu Hatt
Adrien Depeursinge
2023-03-17
Lecture Notes in Computer Science (published)
doi.org
Behavioral Cloning for Crystal Design
Prashant Govindarajan
Santiago Miret
Jarrid Rector-Brooks
Mariano Phielipp
Janarthanan Rajendran
A. Chandar
Solid-state materials, which are made up of periodic 3D crystal structures, are particularly useful for a variety of real-world applications… (see more) such as batteries, fuel cells and catalytic materials. Designing solid-state materials, especially in a robust and automated fashion, remains an ongoing challenge. To further the automated design of crystalline materials, we propose a method to learn to design valid crystal structures given a crystal skeleton. By incorporating Euclidean equivariance into a policy network, we portray the problem of designing new crystals as a sequential prediction task suited for imitation learning. At each step, given an incomplete graph of a crystal skeleton, an agent assigns an element to a specific node. We adopt a behavioral cloning strategy to train the policy network on data consisting of curated trajectories generated from known crystals.
2023-03-16
ICLR.cc/2023/Workshop/ML4Materials (poster)
openreview.net
openreview.net
Spatial Hard Attention Modeling via Deep Reinforcement Learning for Skeleton-Based Human Activity Recognition
Bahareh Nikpour
Narges Armanfard
Deep learning-based algorithms have been very successful in skeleton-based human activity recognition. Skeleton data contains 2-D or 3-D coo… (see more)rdinates of human body joints. The main focus of most of the existing skeleton-based activity recognition methods is on designing new deep architectures to learn discriminative features, where all body joints are considered equally important in recognition. However, the importance of joints varies as an activity proceeds within a video and across different activities. In this work, we hypothesize that selecting relevant joints, prior to recognition, can enhance performance of the existing deep learning-based recognition models. We propose a spatial hard attention finding method that aims to remove the uninformative and/or misleading joints at each frame. We formulate the joint selection problem as a Markov decision process and employ deep reinforcement learning to train the proposed spatial-attention-aware agent. No extra labels are needed for the agent’s training. The agent takes a sequence of features extracted from skeleton video as input and outputs a sequence of probabilities for joints. The proposed method can be considered as a general framework that can be integrated with the existing skeleton-based activity recognition methods for performance improvement purposes. We obtain very competitive activity recognition results on three commonly used human activity recognition datasets.
2023-03-16
IEEE Transactions on Systems, Man, and Cybernetics: Systems (unknown)
doi.org
Transitions between cognitive topographies: contributions of network structure, neuromodulation, and disease
Andrea I. Luppi
S. Parker Singleton
Justine Y. Hansen
Danilo Bzdok
Amy Kuceyeski
Richard F. Betzel
Bratislav Misic
Patterns of neural activity underlie human cognition. Transitions between these patterns are orchestrated by the brain’s network architect… (see more)ure. What are the mechanisms linking network structure to cognitively relevant activation patterns? Here we implement principles of network control to investigate how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic engine. We also systematically incorporate neurotransmitter receptor density maps (18 receptors and transporters) and disease-related cortical abnormality maps (11 neurodegenerative, psychiatric and neurodevelopmental diseases; N = 17 000 patients, N = 22 000 controls). Integrating large-scale multimodal neuroimaging data from functional MRI, diffusion tractography, cortical morphometry, and positron emission tomography, we simulate how anatomically-guided transitions between cognitive states can be reshaped by pharmacological or pathological perturbation. Our results provide a comprehensive look-up table charting how brain network organisation and chemoarchitecture interact to manifest different cognitive topographies. This computational framework establishes a principled foundation for systematically identifying novel ways to promote selective transitions between desired cognitive topographies.
2023-03-16
bioRxiv (preprint)
doi.org
Instance-Conditioned GAN Data Augmentation for Representation Learning
Pietro Astolfi
Arantxa Casanova
Jakob Verbeek
P Vincent
Adriana Romero
Michal Drozdzal
2023-03-15
ArXiv (preprint)
doi.org
arxiv.org
Analysis of gene expression and use of connectivity mapping to identify drugs for treatment of human glomerulopathies
Chen-Fang Chung
Joan Papillon
José R. Navarro-Betancourt
Julie Guillemette
Ameya Bhope
Amin Emad
Andrey V. Cybulsky
2023-03-12
Frontiers in Medicine (published)
doi.org
Applying the column generation method to the intensity modulated high dose rate brachytherapy inverse planning problem
Majd Antaki
Marc-André Renaud
Marc Morcos
Jan Seuntjens
S. Enger
2023-03-12
Physics in Medicine & Biology (published)
doi.org
Investigating the neural correlates of affective mentalizing and their association with general intelligence in patients with schizophrenia
Wladimir Tantchik
Melissa J. Green
Yann Quidé
Susanne Erk
Sebastian Mohnke
Carolin Wackerhagen
Nina Romanczuk-Seiferth
Heike Tost
Kristina Schwarz
Carolin Moessnang
Danilo Bzdok
Andreas Meyer-Lindenberg
Andreas Heinz
Henrik Walter
2023-03-12
Schizophrenia Research (unknown)
doi.org
Optimization of the location and design of urban green spaces
Caroline Leboeuf
Margarida Carvalho
Yan Kestens
Benoit Thierry
2023-03-12
ArXiv (preprint)
doi.org
arxiv.org
Proactive Contact Tracing
Prateek Gupta
Tegan Maharaj
Martin Weiss
Nasim Rahaman
Hannah Alsdurf
Nanor Minoyan
Soren Harnois-Leblanc
Joanna Merckx
Andrew Williams
Victor Schmidt
Pierre-Luc St-Charles
Akshay Patel
Yang Zhang
David L. Buckeridge
Christopher Pal
Bernhard Schölkopf
Yoshua Bengio
The COVID-19 pandemic has spurred an unprecedented demand for interventions that can reduce disease spread without excessively restricting d… (see more)aily activity, given negative impacts on mental health and economic outcomes. Digital contact tracing (DCT) apps have emerged as a component of the epidemic management toolkit. Existing DCT apps typically recommend quarantine to all digitally-recorded contacts of test-confirmed cases. Over-reliance on testing may, however, impede the effectiveness of such apps, since by the time cases are confirmed through testing, onward transmissions are likely to have occurred. Furthermore, most cases are infectious over a short period; only a subset of their contacts are likely to become infected. These apps do not fully utilize data sources to base their predictions of transmission risk during an encounter, leading to recommendations of quarantine to many uninfected people and associated slowdowns in economic activity. This phenomenon, commonly termed as “pingdemic,” may additionally contribute to reduced compliance to public health measures. In this work, we propose a novel DCT framework, Proactive Contact Tracing (PCT), which uses multiple sources of information (e.g. self-reported symptoms, received messages from contacts) to estimate app users’ infectiousness histories and provide behavioral recommendations. PCT methods are by design proactive, predicting spread before it occurs. We present an interpretable instance of this framework, the Rule-based PCT algorithm, designed via a multi-disciplinary collaboration among epidemiologists, computer scientists, and behavior experts. Finally, we develop an agent-based model that allows us to compare different DCT methods and evaluate their performance in negotiating the trade-off between epidemic control and restricting population mobility. Performing extensive sensitivity analysis across user behavior, public health policy, and virological parameters, we compare Rule-based PCT to i) binary contact tracing (BCT), which exclusively relies on test results and recommends a fixed-duration quarantine, and ii) household quarantine (HQ). Our results suggest that both BCT and Rule-based PCT improve upon HQ, however, Rule-based PCT is more efficient at controlling spread of disease than BCT across a range of scenarios. In terms of cost-effectiveness, we show that Rule-based PCT pareto-dominates BCT, as demonstrated by a decrease in Disability Adjusted Life Years, as well as Temporary Productivity Loss. Overall, we find that Rule-based PCT outperforms existing approaches across a varying range of parameters. By leveraging anonymized infectiousness estimates received from digitally-recorded contacts, PCT is able to notify potentially infected users earlier than BCT methods and prevent onward transmissions. Our results suggest that PCT-based applications could be a useful tool in managing future epidemics.
2023-03-12
PLOS Digital Health (published)
doi.org
Neural manifolds and learning regimes in neural-interface tasks
Alexandre Payeur
Amy L. Orsborn
Guillaume Lajoie
Neural activity tends to reside on manifolds whose dimension is lower than the dimension of the whole neural state space. Experiments using … (see more)brain-computer interfaces (BCIs) with microelectrode arrays implanted in the motor cortex of nonhuman primates have provided ways to test whether neural manifolds influence learning-related neural computations. Starting from a learned BCI-controlled motor task, these experiments explored the effect of changing the BCI decoder to implement perturbations that were either “aligned” or not with the pre-existing neural manifold. In a series of studies, researchers found that within-manifold perturbations (WMPs) evoked fast reassociations of existing neural patterns for rapid adaptation, while outside-manifold perturbations (OMPs) triggered a slower adaptation process that led to the emergence of new neural patterns. Together, these findings have been interpreted as suggesting that these different rates of adaptation might be associated with distinct learning mechanisms. Here, we investigated whether gradient-descent learning could alone explain these differences. Using an idealized model that captures the fixed-point dynamics of recurrent neural networks, we uncovered gradient-based learning dynamics consistent with experimental findings. Crucially, this experimental match arose only when the network was initialized in a lazier learning regime, a concept inherited from deep learning theory. A lazy learning regime—in contrast with a rich regime—implies small changes on synaptic strengths throughout learning. For OMPs, these small changes were less effective at increasing performance and could lead to unstable adaptation with a heightened sensitivity to learning rates. For WMPs, they helped reproduce the reassociation mechanism on short adaptation time scales, especially with large input variances. Since gradient descent has many biologically plausible variants, our findings establish lazy gradient-based learning as a plausible mechanism for adaptation under network-level constraints and unify several experimental results from the literature.
2023-03-11
bioRxiv (preprint)
doi.org
A Systematic Study of Joint Representation Learning on Protein Sequences and Structures
Zuobai Zhang
Chuanrui Wang
Minghao Xu
Vijil Chenthamarakshan
Aurelie Lozano
Payel Das
Jian Tang
Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions. Recent sequenc… (see more)e representation learning methods based on Protein Language Models (PLMs) excel in sequence-based tasks, but their direct adaptation to tasks involving protein structures remains a challenge. In contrast, structure-based methods leverage 3D structural information with graph neural networks and geometric pre-training methods show potential in function prediction tasks, but still suffers from the limited number of available structures. To bridge this gap, our study undertakes a comprehensive exploration of joint protein representation learning by integrating a state-of-the-art PLM (ESM-2) with distinct structure encoders (GVP, GearNet, CDConv). We introduce three representation fusion strategies and explore different pre-training techniques. Our method achieves significant improvements over existing sequence- and structure-based methods, setting new state-of-the-art for function annotation. This study underscores several important design choices for fusing protein sequence and structure information. Our implementation is available at https://github.com/DeepGraphLearning/ESM-GearNet.
2023-03-10
ArXiv (preprint)
arxiv.org
arxiv.org