Publications

Contrast-agnostic Spinal Cord Segmentation: A Comparative Study of ConvNets and Vision Transformers

Enamundram Naga Karthik

Sandrine Bédard

Jan Valošek

Sarath Chandar

Julien Cohen-Adad

The cross-sectional area (CSA) of the spinal cord (SC) computed from its segmentation is a relevant clinical biomarker for the diagnosis and… (voir plus) monitoring of cord compression and atrophy. One key limitation of existing automatic methods is that their SC segmentations depend on the MRI contrast, resulting in different CSA across contrasts. Furthermore, these methods rely on CNNs, leaving a gap in the literature for exploring the performance of modern deep learning (DL) architectures. In this study, we extend our recent work \cite{Bdard2023TowardsCS} by evaluating the contrast-agnostic SC segmentation capabilities of different classes of DL architectures, namely, ConvNeXt, vision transformers (ViTs), and hierarchical ViTs. We compared 7 different DL models using the open-source \textit{Spine Generic} Database of healthy participants

2024-04-27

MIDL.io/2024/Short_Papers (accepté)

Human local field potentials in motor and non-motor brain areas encode upcoming movement direction.

Etienne Combrisson

Franck Di Rienzo

Anne-Lise Saive

Marcela Perrone-Bertolotti

Juan LP Soto

Philippe Kahane

Jean-Philippe Lachaux

Aymeric Guillot

Karim Jerbi

2024-04-27

Communications Biology (publié)

Discrete Probabilistic Inference as Control in Multi-path Environments

Tristan Deleu

Padideh Nouri

Nikolay Malkin

Doina Precup

Yoshua Bengio

We consider the problem of sampling from a discrete and structured distribution as a sequential decision problem, where the objective is to … (voir plus)find a stochastic policy such that objects are sampled at the end of this sequential process proportionally to some predefined reward. While we could use maximum entropy Reinforcement Learning (MaxEnt RL) to solve this problem for some distributions, it has been shown that in general, the distribution over states induced by the optimal policy may be biased in cases where there are multiple ways to generate the same object. To address this issue, Generative Flow Networks (GFlowNets) learn a stochastic policy that samples objects proportionally to their reward by approximately enforcing a conservation of flows across the whole Markov Decision Process (MDP). In this paper, we extend recent methods correcting the reward in order to guarantee that the marginal distribution induced by the optimal MaxEnt RL policy is proportional to the original reward, regardless of the structure of the underlying MDP. We also prove that some flow-matching objectives found in the GFlowNet literature are in fact equivalent to well-established MaxEnt RL algorithms with a corrected reward. Finally, we study empirically the performance of multiple MaxEnt RL and GFlowNet algorithms on multiple problems involving sampling from discrete distributions.

2024-04-26

auai.org/UAI/2024/Conference (poster)

Neural Active Learning Meets the Partial Monitoring Framework

Maxime Heuillet

Ola Ahmad

Audrey Durand

We focus on the online-based active learning (OAL) setting where an agent operates over a stream of observations and trades-off between the … (voir plus)costly acquisition of information (labelled observations) and the cost of prediction errors. We propose a novel foundation for OAL tasks based on partial monitoring, a theoretical framework specialized in online learning from partially informative actions. We show that previously studied binary and multi-class OAL tasks are instances of partial monitoring. We expand the real-world potential of OAL by introducing a new class of cost-sensitive OAL tasks. We propose NeuralCBP, the first PM strategy that accounts for predictive uncertainty with deep neural networks. Our extensive empirical evaluation on open source datasets shows that NeuralCBP has favorable performance against state-of-the-art baselines on multiple binary, multi-class and cost-sensitive OAL tasks.

2024-04-26

auai.org/UAI/2024/Conference (poster)

Neural Active Learning Meets the Partial Monitoring Framework

Maxime Heuillet

Ola Ahmad

Audrey Durand

2024-04-26

auai.org/UAI/2024/Conference (poster)

Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization.

Hossein Jafarzadeh

Majd Antaki

Ximeng Mao

Marie Duclos

Farhad Maleki

Shirin A. Enger

OBJECTIVE Treatment plan optimization in high dose rate (HDR) brachytherapy often requires manual fine-tuning of penalty weights for each ob… (voir plus)jective, which can be time-consuming and dependent on the planner's experience. To automate this process, this study used a multi-criteria approach called multi-objective Bayesian optimization with q-noisy expected hypervolume improvement as its acquisition function (MOBO-qNEHVI). Approach: The treatment plans of 13 prostate cancer patients were retrospectively imported to a research treatment planning system, RapidBrachyMTPS, where fast mixed integer optimization (FMIO) performs dwell time optimization given a set of penalty weights to deliver 15 Gy to the target volume. MOBO-qNEHVI was used to find patient-specific Pareto optimal penalty weight vectors that yield clinically acceptable dose volume histogram metrics. The relationship between the number of MOBO-qNEHVI iterations and the number of clinically acceptable plans per patient (acceptance rate) was investigated. The performance time was obtained for various parameter configurations. Main results: MOBO-qNEHVI found clinically acceptable treatment plans for all patients. With increasing the number of MOBO-qNEHVI iterations, the acceptance rate grew logarithmically while the performance time grew exponentially. Fixing the penalty weight of the tumour volume to maximum value, adding the target dose as a parameter, initiating MOBO-qNEHVI with 25 parallel sampling of FMIO, and running 6 MOBO-qNEHVI iterations found solutions that delivered 15 Gy to the hottest 95% of the clinical target volume while respecting the dose constraints to the organs at risk. The average acceptance rate for each patient was 89.74% ± 8.11%, and performance time was 66.6 ± 12.6 seconds. The initiation took 22.47 ± 7.57 s, and each iteration took 7.35 ± 2.45 s to find one Pareto solution. Significance: MOBO-qNEHVI can automatically explore the trade-offs between treatment plan objectives in a patient-specific manner within a minute. This approach can reduce the dependency of plan quality on planner's experience.

2024-04-26

Physics in Medicine and Biology (publié)

Autoregressive Networks with Dependent Edges

Jinyuan Chang

Qin Fang

Eric Kolaczyk

Peter W. MacDonald

Qiwei Yao

2024-04-24

ArXiv (prépublication)

Radiation hardness of open Fabry-Pérot microcavities

Fernanda C. Rodrigues-Machado

Erika Janitz

Simon Bernard

H. Bekerat

Malcolm McEwen

James Renaud

Shirin A. Enger

Lilian Childress

Jack C Sankey

2024-04-24

Optics Express (publié)

SPARO: Selective Attention for Robust and Compositional Transformer Encodings for Vision

Ankit Vani

Bac Nguyen

Samuel Lavoie

Ranjay Krishna

Aaron Courville

Selective attention helps us focus on task-relevant aspects in the constant flood of our sensory input. This constraint in our perception al… (voir plus)lows us to robustly generalize under distractions and to new compositions of perceivable concepts. Transformers employ a similar notion of attention in their architecture, but representation learning models with transformer backbones like CLIP and DINO often fail to demonstrate robustness and compositionality. We highlight a missing architectural prior: unlike human perception, transformer encodings do not separately attend over individual concepts. In response, we propose SPARO, a read-out mechanism that partitions encodings into separately-attended slots, each produced by a single attention head. Using SPARO with CLIP imparts an inductive bias that the vision and text modalities are different views of a shared compositional world with the same corresponding concepts. Using SPARO, we demonstrate improvements on downstream recognition, robustness, retrieval, and compositionality benchmarks with CLIP (up to +14% for ImageNet, +4% for SugarCrepe), and on nearest neighbors and linear probe for ImageNet with DINO (+3% each). We also showcase a powerful ability to intervene and select individual SPARO concepts to further improve downstream task performance (up from +4% to +9% for SugarCrepe) and use this ability to study the robustness of SPARO's representation structure. Finally, we provide insights through ablation experiments and visualization of learned concepts.

2024-04-24

ArXiv (prépublication)

Universal Adversarial Triggers Are Not Universal

Nicholas Meade

Arkil Patel

Siva Reddy

2024-04-24

ArXiv (prépublication)

XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference

Jo˜ao Monteiro

Étienne Marcotte

Pierre-Andre Noel

Valentina Zantedeschi

David Vazquez

Nicolas Chapados

Chris Pal

Perouz Taslakian

In-context learning (ICL) approaches typically leverage prompting to condition decoder-only language model generation on reference informati… (voir plus)on. Just-in-time processing of a context is inefficient due to the quadratic cost of self-attention operations, and caching is desirable. However, caching transformer states can easily require almost as much space as the model parameters. When the right context isn't known in advance, caching ICL can be challenging. This work addresses these limitations by introducing models that, inspired by the encoder-decoder architecture, use cross-attention to condition generation on reference text without the prompt. More precisely, we leverage pre-trained decoder-only models and only train a small number of added layers. We use Question-Answering (QA) as a testbed to evaluate the ability of our models to perform conditional generation and observe that they outperform ICL, are comparable to fine-tuned prompted LLMs, and drastically reduce the space footprint relative to standard KV caching by two orders of magnitude.

2024-04-23

ArXiv (prépublication)