Publications

Semi-Supervised Object Detection for Agriculture

Gabriel Tseng

Krisztina Sinkovics

Tom Watsham

David Rolnick

Thomas C. Walters

2023-01-26

AAAI.org/2023/Workshop/AIAFS (poster)

Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach

Reza Azad

Yiwei Jia

Ehsan Khodapanah Aghdam

Julien Cohen-Adad

Dorit Merhof

While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness, they suff… (voir plus)er from limitations in capturing long-range dependencies. Transformer-based approaches are currently prevailing since they enlarge the reception field to model global contextual correlation. To further extract rich representations, some extensions of the U-Net employ multi-scale feature extraction and fusion modules and obtain improved performance. Inspired by this idea, we propose TransCeption for medical image segmentation, a pure transformer-based U-shape network featured by incorporating the inception-like module into the encoder and adopting a contextual bridge for better feature fusion. The design proposed in this work is based on three core principles: (1) The patch merging module in the encoder is redesigned with ResInception Patch Merging (RIPM). Multi-branch transformer (MB transformer) adopts the same number of branches as the outputs of RIPM. Combining the two modules enables the model to capture a multi-scale representation within a single stage. (2) We construct an Intra-stage Feature Fusion (IFF) module following the MB transformer to enhance the aggregation of feature maps from all the branches and particularly focus on the interaction between the different channels of all the scales. (3) In contrast to a bridge that only contains token-wise self-attention, we propose a Dual Transformer Bridge that also includes channel-wise self-attention to exploit correlations between scales at different stages from a dual perspective. Extensive experiments on multi-organ and skin lesion segmentation tasks present the superior performance of TransCeption compared to previous work. The code is publicly available at https://github.com/mindflow-institue/TransCeption.

2023-01-25

ArXiv (prépublication)

Samira Abbasgholizadeh-Rahimi

Explainable Machine Learning Model to Predict COVID-19 Severity Among Older Adults in the Province of Quebec.

Charlene H Chu

Roland M. Grad

Mark Karanofsky

Mylene Arsenault

Charlene Esteban Ronquillo

Isabelle Vedel

K. McGilton

Machelle Wilchesky

Context: Patients over the age of 65 years are more likely to experience higher severity and mortality rates than other populations from COV… (voir plus)ID-19. Clinicians need assistance in supporting their decisions regarding the management of these patients. Artificial Intelligence (AI) can help with this regard. However, the lack of explainability-defined as "the ability to understand and evaluate the internal mechanism of the algorithm/computational process in human terms"-of AI is one of the major challenges to its application in health care. We know little about application of explainable AI (XAI) in health care. Objective: In this study, we aimed to evaluate the feasibility of the development of explainable machine learning models to predict COVID-19 severity among older adults. Design: Quantitative machine learning methods. Setting: Long-term care facilities within the province of Quebec. Participants: Patients 65 years and older presented to the hospitals who had a positive polymerase chain reaction test for COVID-19. Intervention: We used XAI-specific methods (e.g., EBM), machine learning methods (i.e., random forest, deep forest, and XGBoost), as well as explainable approaches such as LIME, SHAP, PIMP, and anchor with the mentioned machine learning methods. Outcome measures: Classification accuracy and area under the receiver operating characteristic curve (AUC). Results: The age distribution of the patients (n=986, 54.6% male) was 84.5□19.5 years. The best-performing models (and their performance) were as follows. Deep forest using XAI agnostic methods LIME (97.36% AUC, 91.65 ACC), Anchor (97.36% AUC, 91.65 ACC), and PIMP (96.93% AUC, 91.65 ACC). We found alignment with the identified reasoning of our models' predictions and clinical studies' findings-about the correlation of different variables such as diabetes and dementia, and the severity of COVID-19 in this population. Conclusions: The use of explainable machine learning models, to predict the severity of COVID-19 among older adults is feasible. We obtained a high-performance level as well as explainability in the prediction of COVID-19 severity in this population. Further studies are required to integrate these models into a decision support system to facilitate the management of diseases such as COVID-19 for (primary) health care providers and evaluate their usability among them.

2023-01-24

Big data (publié)

Regeneration Learning: A Learning Paradigm for Data Generation

Xu Tan

Tao Qin

Jiang Bian

Tie-Yan Liu

Yoshua Bengio

2023-01-21

ArXiv (prépublication)

Robustness and Sample Complexity of Model-Based MARL for General-Sum Markov Games

Jayakumar Subramanian

Amit Sinha

Aditya Mahajan

2023-01-21

Dynamic Games and Applications (publié)

Disentangling poststroke cognitive deficits and their neuroanatomical correlates through combined multivariable and multioutcome lesion‐symptom mapping

Nick A. Weaver

Muhammad Hasnain Mamdani

Jae‐Sung Lim

J. Matthijs Biesbroek

Geert Jan Biessels

Irene M. C. Huenges Wajer

Yeonwook Kang

Beom Joon Kim

Byung‐Chul Lee

Keon‐Joo Lee

Kyung‐Ho Yu

Hee-Joon Bae

Danilo Bzdok

Hugo J. Kuijf

2023-01-20

Human Brain Mapping (publié)

lo-fi: distributed fine-tuning without communication

Mitchell Wortsman

Suchin Gururangan

Shen Li

Ali Farhadi

Ludwig Schmidt

Michael Rabbat

Ari S. Morcos

When fine-tuning large neural networks, it is common to use multiple nodes and to communicate gradients at each optimization step. By contra… (voir plus)st, we investigate completely local fine-tuning, which we refer to as lo-fi. During lo-fi, each node fine-tunes independently without any communication. Then, the weights are averaged across nodes at the conclusion of fine-tuning. When fine-tuning DeiT-base and DeiT-large on ImageNet, this procedure matches accuracy in-distribution and improves accuracy under distribution shift compared to the baseline, which observes the same amount of data but communicates gradients at each step. We also observe that lo-fi matches the baseline's performance when fine-tuning OPT language models (up to 1.3B parameters) on Common Crawl. By removing the communication requirement, lo-fi reduces resource barriers for fine-tuning large models and enables fine-tuning in settings with prohibitive communication cost.

2023-01-20

TMLR (accepté)

Laurence Perreault-Levasseur

A Framework for Obtaining Accurate Posteriors of Strong Gravitational Lensing Parameters with Flexible Priors and Implicit Likelihoods Using Density Estimation

Ronan Legin

Yashar Hezaveh

Benjamin Wandelt

We report the application of implicit likelihood inference to the prediction of the macroparameters of strong lensing systems with neural ne… (voir plus)tworks. This allows us to perform deep-learning analysis of lensing systems within a well-defined Bayesian statistical framework to explicitly impose desired priors on lensing variables, obtain accurate posteriors, and guarantee convergence to the optimal posterior in the limit of perfect performance. We train neural networks to perform a regression task to produce point estimates of lensing parameters. We then interpret these estimates as compressed statistics in our inference setup and model their likelihood function using mixture density networks. We compare our results with those of approximate Bayesian neural networks, discuss their significance, and point to future directions. Based on a test set of 100,000 strong lensing simulations, our amortized model produces accurate posteriors for any arbitrary confidence interval, with a maximum percentage deviation of 1.4% at the 21.8% confidence level, without the need for any added calibration procedure. In total, inferring 100,000 different posteriors takes a day on a single GPU, showing that the method scales well to the thousands of lenses expected to be discovered by upcoming sky surveys.

2023-01-19

The Astrophysical Journal (publié)

Label fusion and training methods for reliable representation of inter-rater uncertainty

Andreanne Lemay

Charley Gros

Julien Cohen-Adad

Enamundram Naga Karthik

2023-01-18

Machine Learning for Biomedical Imaging (publié)

From IID to the Independent Mechanisms assumption in continual learning

Oleksiy Ostapenko

Pau Rodriguez

Alexandre Lacoste

Laurent Charlin

Current machine learning algorithms are successful in learning clearly defined tasks from large i.i.d. data. Continual learning (CL) require… (voir plus)s learning without iid-ness and developing algorithms capable of knowledge retention and transfer, the latter can be boosted through systematic generalization. Dropping the i.i.d. assumption requires replacing it with another hypothesis. While there are several candidates, here we advocate that the independent mechanism assumption (IM) (Sch¨olkopf et al., 2012) is a useful hypothesis for representing knowledge in a form, that makes it easy to adapt to new tasks in CL. Specifically, we review several types of distribution shifts that are common in CL and point out in which way a system that represents knowledge in the form of causal modules may outperform monolithic counterparts in CL. Intuitively, the efficacy of IM solution emerges since (i) causal modules learn mechanisms invariant across domains; (ii) if causal mechanisms must be updated, modularity can enable efficient and sparse updates.

2023-01-11

AAAI.org/2023/Bridge/CCBridge (publié)

From IID to the Independent Mechanisms assumption in continual learning

Oleksiy Ostapenko

Pau Rodriguez

Alexandre Lacoste

Laurent Charlin

2023-01-11

AAAI.org/2023/Bridge/CCBridge (accepté)

proceedings.mlr.press