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Ximeng Mao

ximeng.mao@mila.quebec
Doctorat - Université de Montréal
Superviseur⋅e principal⋅e
Guillaume Lajoie
Co-superviseur⋅e
Joelle Pineau
Github
Google Scholar

Publications

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)
doi.org
Personalized inference for neurostimulation with meta-learning: a case study of vagus nerve stimulation
Ximeng Mao
Yao-Chuan Chang
Stavros Zanos
Guillaume Lajoie
2024-01-12
Journal of Neural Engineering (publié)
doi.org
A Unified, Scalable Framework for Neural Population Decoding
Mehdi Azabou
Vinam Arora
Venkataramana Ganesh
Ximeng Mao
Santosh B Nachimuthu
Michael Jacob Mendelson
Blake Richards
Matt Perich
Guillaume Lajoie
Eva L Dyer
Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both the model … (voir plus)size and the datasets. However, the integration of many neural recordings into one unified model is challenging, as each recording contains the activity of different neurons from different individual animals. In this paper, we introduce a training framework and architecture designed to model the population dynamics of neural activity across diverse, large-scale neural recordings. Our method first tokenizes individual spikes within the dataset to build an efficient representation of neural events that captures the fine temporal structure of neural activity. We then employ cross-attention and a PerceiverIO backbone to further construct a latent tokenization of neural population activities. Utilizing this architecture and training framework, we construct a large-scale multi-session model trained on large datasets from seven nonhuman primates, spanning over 158 different sessions of recording from over 27,373 neural units and over 100 hours of recordings. In a number of different tasks, we demonstrate that our pretrained model can be rapidly adapted to new, unseen sessions with unspecified neuron correspondence, enabling few-shot performance with minimal labels. This work presents a powerful new approach for building deep learning tools to analyze neural data and stakes out a clear path to training at scale for neural decoding models.
openreview.net
Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer
Damjan Kalajdzievski
Ximeng Mao
Pascal Fortier-Poisson
Guillaume Lajoie
Blake Richards
When presented with a data stream of two statistically dependent variables, predicting the future of one of the variables (the target stream… (voir plus)) can benefit from information about both its history and the history of the other variable (the source stream). For example, fluctuations in temperature at a weather station can be predicted using both temperatures and barometric readings. However, a challenge when modelling such data is that it is easy for a neural network to rely on the greatest joint correlations within the target stream, which may ignore a crucial but small information transfer from the source to the target stream. As well, there are often situations where the target stream may have previously been modelled independently and it would be useful to use that model to inform a new joint model. Here, we develop an information bottleneck approach for conditional learning on two dependent streams of data. Our method, which we call Transfer Entropy Bottleneck (TEB), allows one to learn a model that bottlenecks the directed information transferred from the source variable to the target variable, while quantifying this information transfer within the model. As such, TEB provides a useful new information bottleneck approach for modelling two statistically dependent streams of data in order to make predictions about one of them.
2023-03-08
TMLR (accepté)
doi.org
openreview.net
Rapidly Inferring Personalized Neurostimulation Parameters with Meta-Learning: A Case Study of Individualized Fiber Recruitment in Vagus Nerve Stimulation
Ximeng Mao
Yao-Chuan Chang
Stavros Zanos
Guillaume Lajoie
2022-09-08
bioRxiv (prépublication)
doi.org