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Using Computer Vision and Artificial Intelligence to Track the Healing of Severe Burns

JOURNAL OF BURN CARE & RESEARCH 2024年第3期45卷 700-708页

作者： Ethier, Olivier Chan, Hannah O. Abdolahnejad, Mahla Morzycki, Alexander Tchango, Arsene Fansi Joshi, Rakesh Wong, Joshua N. Hong, Collin Univ Montreal Montreal Inst Learning Algorithms MILA Montreal PQ H2S 3H1 Canada Skinopathy Inc Skinopathy Res N York ON M2N1N5 Canada Centenary Hosp Scarborough Hlth Network Scarborough ON M1E 4B9 Canada Univ Alberta Hosp Dept Surg Edmonton AB T6G 2B7 Canada

Burn care management includes assessing the severity of burns accurately, especially distinguishing superficial partial-thickness burns from deep partial-thickness burns, in the context of providing definitive, downstream treatment. Moreover, the healing of the wound in the subacute care setting requires continuous tracking to avoid complications. Artificial intelligence (AI) and computer vision (CV) provide a unique opportunity to build low-cost and accessible tools to classify burn severity and track changes in wound parameters, both in the clinic by physicians and nurses and asynchronously in the remote setting by the patient themselves. Wound assessments can be achieved by AI-CV using the principles of image-guided therapy using high-quality 2D color images. Wound parameters can include wound 2D spatial dimension and the characterization of wound color changes, which demonstrates physiological changes such as the presentation of eschar/necrotic tissue, pustulence, granulation tissue, and scabbing. Here we present the development of AI-CV-based Skin Abnormality Tracking Algorithm pipeline. Additionally, we provide the results on a single localized burn tracked for a 6-week period in the clinic and an additional 2-week period of home monitoring.

关键词： artificial intelligence burn assessments wound healing remote monitoring

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A Two-Stream Continual learning System With Variational Domain-Agnostic Feature Replay

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2022年第9期33卷 4466-4478页

作者： Lao, Qicheng Jiang, Xiang Havaei, Mohammad Bengio, Yoshua Sichuan Univ West China Hosp West China Biomed Big Data Ctr Chengdu 610041 Peoples R China Univ Montreal Montreal Inst Learning Algorithms MILA Montreal PQ H2S 3H1 Canada Imagia Montreal PQ H2S 3G9 Canada

learning in nonstationary environments is one of the biggest challenges in machine learning. Nonstationarity can be caused by either task drift, i.e., the drift in the conditional distribution of labels given the input data, or the domain drift, i.e., the drift in the marginal distribution of the input data. This article aims to tackle this challenge with a modularized two-stream continual learning (CL) system, where the model is required to learn new tasks from a support stream and adapted to new domains in the query stream while maintaining previously learned knowledge. To deal with both drifts within and across the two streams, we propose a variational domain-agnostic feature replay-based approach that decouples the system into three modules: an inference module that filters the input data from the two streams into domain-agnostic representations, a generative module that facilitates the high-level knowledge transfer, and a solver module that applies the filtered and transferable knowledge to solve the queries. We demonstrate the effectiveness of our proposed approach in addressing the two fundamental scenarios and complex scenarios in two-stream CL.

关键词： Task analysis Training learning systems Knowledge transfer Image color analysis Shape Mutual information Continual learning (CL) generative replay learning systems nonstationary environments unsupervised domain adaptation variational inference

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Full-Scale Information Diffusion Prediction With Reinforced Recurrent Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2023年第5期34卷 2271-2283页

作者： Yang, Cheng Wang, Hao Tang, Jian Shi, Chuan Sun, Maosong Cui, Ganqu Liu, Zhiyuan Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China Mila Quebec Inst Learning Algorithms Montreal PQ H2S 3H1 Canada HEC Montreal Montreal PQ H3T 2A7 Canada Canadian Inst Adv Res CIFAR Toronto ON M5G 1Z8 Canada Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

Information diffusion prediction is an important task, which studies how information items spread among users. With the success of deep learning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction, which aims at guessing who will be the next influenced user at what time, or macroscopic diffusion prediction, which estimates the total numbers of influenced users during the diffusion process. To the best of our knowledge, few attempts have been made to suggest a unified model for both microscopic and macroscopic scales. In this article, we propose a novel full-scale diffusion prediction model based on reinforcement learning (RL). RL incorporates the macroscopic diffusion size information into the RNN-based microscopic diffusion model by addressing the nondifferentiable problem. We also employ an effective structural context extraction strategy to utilize the underlying social graph information. Experimental results show that our proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic diffusion predictions on three real-world datasets.

关键词： Microscopy Predictive models Task analysis Integrated circuit modeling Data mining Recurrent neural networks Feature extraction Information diffusion prediction recurrent neural networks (RNNs) reinforcement learning (RL)

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Novel CNN-Based Approach for Burn Severity Assessment and Fine-Grained Boundary Segmentation in Burn Images

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IEEE TRANSACTIONS ON instRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Abdolahnejad, Mahla Lee, Justin Chan, Hannah Morzycki, Alex Ethier, Olivier Mo, Anthea Liu, Peter X. Wong, Joshua N. Hong, Colin Joshi, Rakesh Skinopathy Inc Skinopathy Res N York ON M2N 1N5 Canada Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S5B6 Canada Univ Alberta Dept Surg Div Plast & Reconstruct Surg Edmonton AB T6G 2B7 Canada Centenary Hosp Scarborough Hlth Network Scarborough ON M1E 4B9 Canada Univ Montreal Montreal Inst Learning Algorithms MILA Montreal PQ H2S 3H1 Canada

Burn injuries, resulting from thermal, chemical, and electrical mechanisms, require prompt and accurate assessment for effective treatment. The primary method, relying on visual and tactile evaluations, offers 50%-80% accuracy, while noninvasive methods such as laser Doppler imaging (LDI) reach up to 97% accuracy. This article presents a machine learning (ML) pipeline for assessing burn severity and segmenting affected skin regions. We trained a convolutional neural network (CNN) to classify four burn severities: superficial (SPF), superficial partial thickness (SPT), deep partial thickness (DPT), and full thickness (FT). In addition, we introduced boundary attention mapping (BAM), a saliency mapping method that leverages the trained CNN to accurately segment burn regions. Our pipeline was validated using two datasets: a Burn Injury Image dataset with 1385 images and an LDI dataset with 184 images. The CNN achieved 80% accuracy, a 79.5% average F1-score, and 95% ROC in classifying burn severities. Comparing BAM with LDI, our method achieved 91.39% accuracy, 78.12% sensitivity, and 95.07% specificity in segmenting burn regions. These findings demonstrate the robustness of our AI model and its potential clinical application.

关键词： Accuracy Image segmentation Visualization Heating systems Training Convolutional neural networks Skin burns Mobile applications Correlation Medical diagnostic imaging Burn area measurement burn depth convolutional neural networks (CNNs) machine learning (ML) medical assessment skin burn

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Multimodal End-to-End Autonomous Driving

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第1期23卷 537-547页

作者： Xiao, Yi Codevilla, Felipe Gurram, Akhil Urfalioglu, Onay Lopez, Antonio M. Univ Autonoma Barcelona UAB Comp Vis Ctr CVC Bellaterra 08193 Spain Univ Autonoma Barcelona UAB Comp Sci Dept Bellaterra 08193 Spain Montreal Inst Learning Algorithms MILA Montreal PQ H2S 3H1 Canada HUAWEI Technol Duesseldorf GmbH Automot Engn Lab Munich Res Ctr D-80992 Munich Germany

A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressing the development of AI drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception and maneuver planning and control. On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later are relatively less studied, but are gaining popularity since they are less demanding in terms of sensor data annotation. This paper focuses on end-to-end autonomous driving. So far, most proposals relying on this paradigm assume RGB images as input sensor data. However, AVs will not be equipped only with cameras, but also with active sensors providing accurate depth information (e.g., LiDARs). Accordingly, this paper analyses whether combining RGB and depth modalities, i.e. using RGBD data, produces better end-to-end AI drivers than relying on a single modality. We consider multimodality based on early, mid and late fusion schemes, both in multisensory and single-sensor (monocular depth estimation) settings. Using the CARLA simulator and conditional imitation learning (CIL), we show how, indeed, early fusion multimodality outperforms single-modality.

关键词： Semantics Task analysis Laser radar Autonomous vehicles Cameras Multimodal scene understanding end-to-end autonomous driving imitation learning

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Causal Discovery in Astrophysics: Unraveling Supermassive Black Hole and Galaxy Coevolution

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ASTROPHYSICAL JOURNAL 2025年第2期979卷 212-212页

作者： Jin, Zehao Pasquato, Mario Davis, Benjamin L. Deleu, Tristan Luo, Yu Cho, Changhyun Lemos, Pablo Perreault-Levasseur, Laurence Bengio, Yoshua Kang, Xi Maccio, Andrea Valerio Hezaveh, Yashar New York Univ Abu Dhabi POB 129188 Abu Dhabi U Arab Emirates Mohammed Bin Rashid Space Ctr POB 129188 Dubai U Arab Emirates Montreal Inst Astrophys Data Anal & Machine Learni Montreal PQ Canada Quebec Artificial Intelligence Inst Montreal Inst Learning Algorithms Mila Montreal PQ Canada Univ Montreal Dept Phys 1375 Ave Therese-Lavoie-Roux Montreal PQ Canada Univ Padua Dipartimento Fis & Astron Vicolo Osservatorio 5 Padua Italy Ist Astrofis Spaziale & Fis Cosm INAF IASF MI Via Alfonso Corti 12 I-20133 Milan Italy Univ Montreal Dept Informat & Rech Operat 2920 Chemin Tour Montreal PQ Canada Hunan Normal Univ Sch Phys & Elect Dept Phys Changsha 410081 Peoples R China Purple Mt Observ 10 Yuan Hua Rd Nanjing 210034 Peoples R China Flatiron Inst Ctr Computat Astrophys New York NY USA Zhejiang Univ Inst Astron Hangzhou 310027 Peoples R China Max Planck Inst Astron Konigstuhl 17 Heidelberg Germany

Correlation does not imply causation, but patterns of statistical association between variables can be exploited to infer a causal structure (even with purely observational data) with the burgeoning field of causal discovery. As a purely observational science, astrophysics has much to gain by exploiting these new methods. The supermassive black hole (SMBH)-galaxy interaction has long been constrained by observed scaling relations, which is low-scatter correlations between variables such as SMBH mass and the central velocity dispersion of stars in a host galaxy's bulge. This study, using advanced causal discovery techniques and an up-to-date data set, reveals a causal link between galaxy properties and dynamically measured SMBH masses. We apply a score-based Bayesian framework to compute the exact conditional probabilities of every causal structure that could possibly describe our galaxy sample. With the exact posterior distribution, we determine the most likely causal structures and notice a probable causal reversal when separating galaxies by morphology. In elliptical galaxies, bulge properties (built from major mergers) tend to influence SMBH growth, while, in spiral galaxies, SMBHs are seen to affect host galaxy properties, potentially through feedback in gas-rich environments. For spiral galaxies, SMBHs progressively quench star formation, whereas, in elliptical galaxies, quenching is complete, and the causal connection has reversed. Our findings support theoretical models of hierarchical assembly of galaxies and active galactic nuclei feedback regulating galaxy evolution. Our study suggests the potentiality for further exploration of causal links in astrophysical and cosmological scaling relations, as well as any other observational science.

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VeRNAl: a tool for mining fuzzy network motifs in RNA

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BIOINFORMATICS 2022年第4期38卷 970-976页

作者： Oliver, Carlos Mallet, Vincent Philippopoulos, Pericles Hamilton, William L. Waldispuhl, Jerome McGill Univ Sch Comp Sci Montreal PQ H3A 0E9 Canada Montreal Inst Learning Algorithms MILA Montreal PQ H2S 3H1 Canada Inst Pasteur Dept Struct Biol & Chem Struct Bioinformat Unit CNRSUMR3528C3BIUSR3756 Paris France Paris Sci & Lettres Res Univ Ctr Computat Biol Mines ParisTech F-75272 Paris France McGill Univ Dept Phys Montreal PQ Canada

Motivation: RNA 3D motifs are recurrent substructures, modeled as networks of base pair interactions, which are crucial for understanding structure-function relationships. The task of automatically identifying such motifs is computationally hard, and remains a key challenge in the field of RNA structural biology and network analysis. State-ofthe-art methods solve special cases of the motif problem by constraining the structural variability in occurrences of a motif, and narrowing the substructure search space. Results: Here, we relax these constraints by posing the motif finding problem as a graph representation learning and clustering task. This framing takes advantage of the continuous nature of graph representations to model the flexibility and variability of RNA motifs in an efficient manner. We propose a set of node similarity functions, clustering methods and motif construction algorithms to recover flexible RNA motifs. Our tool, VeRNAl can be easily customized by users to desired levels of motif flexibility, abundance and size. We show that VeRNAl is able to retrieve and expand known classes of motifs, as well as to propose novel motifs.

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Combination Networks With End-User-Caches: Novel Achievable and Converse Bounds Under Uncoded Cache Placement

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第2期68卷 806-827页

作者： Wan, Kai Tuninetti, Daniela Ji, Mingyue Piantanida, Pablo Univ Paris Sud CNRS Cent Supelec Lab Signaux & Syst L2SUMR8506 F-91192 Gif Sur Yvette France Tech Univ Berlin Elect Engn & Comp Sci Dept D-10587 Berlin Germany Univ Illinois Elect & Comp Engn Dept Chicago IL 60607 USA Univ Utah Elect & Comp Engn Dept Salt Lake City UT 84112 USA Univ Montreal Montreal Inst Learning Algorithms MILA Montreal PQ H3T 1N8 Canada

Caching is an efficient way to reduce network traffic congestion during peak hours by storing some content at the users' local caches. For the shared-link network with end-user-caches, Maddah-Ali and Niesen proposed a two-phase coded caching strategy. In practice, users may communicate with the server through intermediate relays. This paper studies the tradeoff between the memory size M and the network load R for the networks where a server with N files is connected to H relays (without caches), which in turn are connected to K users equipped with caches of M files. When each user is connected to a different subset of r relays, i.e., K = (H r), the system is referred to as a combination network with end-user-caches. In this work, converse bounds are derived for the practically motivated case of uncoded cache contents, that is, bits of the various files are directly pushed into the user caches without any coding. In this case, once the cache contents and the users' demands are known, the problem reduces to a general index coding problem. This paper shows that relying on a well-known "acyclic index coding converse bound" results in converse bounds that are not tight for combination networks with end-user-caches. A novel converse bound that leverages the network topology is proposed, which is the tightest converse bound known to date. As a result of independent interest, an inequality that generalizes the well-known sub-modularity of entropy is derived. Several novel caching schemes are proposed, based on the Maddah-Ali and Niesen cache placement. These schemes leverage the structure of the combination network or/and perform interference elimination at the end-users. The proposed schemes are proved: (i) to be (order) optimal for some (N, M, H, r) parameters regimes under the constraint of uncoded cache placement, and (ii) to outperform the state-of-the-art schemes in numerical evaluations.

关键词： Coded caching combination networks uncoded cache placement interference elimination

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Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep learning Inference

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2022年第2期41卷 360-373页

作者： Mehta, Raghav Christinck, Thomas Nair, Tanya Bussy, Aurelie Premasiri, Swapna Costantino, Manuela Chakravarthy, M. Mallar Arnold, Douglas L. Gal, Yarin Arbel, Tal McGill Univ Ctr Intelligent Machines CIM Montreal PQ H3A 0G4 Canada McGill Univ Integrated Program Neurosci Montreal PQ H3A 0G4 Canada Douglas Mental Hlth Univ Inst Comput Brain Anat CoBrA Lab Cerebral Imaging Ctr Montreal PQ H4H 1R3 Canada McGill Univ Montreal Neurol Inst Montreal PQ H3A 0G4 Canada NeuroRx Res Montreal PQ H3A 0G4 Canada Univ Oxford OATML Dept Comp Sci Oxford OX1 2JD England Montreal Inst Learning Algorithms MILA Montreal PQ H2S 3H1 Canada

Although deep networks have been shown to perform very well on a variety of medical imaging tasks, inference in the presence of pathology presents several challenges to common models. These challenges impede the integration of deep learning models into real clinical workflows, where the customary process of cascading deterministic outputs from a sequence of image-based inference steps (e.g. registration, segmentation) generally leads to an accumulation of errors that impacts the accuracy of downstream inference tasks. In this paper, we propose that by embedding uncertainty estimates across cascaded inference tasks, performance on the downstream inference tasks should be improved. We demonstrate the effectiveness of the proposed approach in three different clinical contexts: (i) We demonstrate that by propagating T2 weighted lesion segmentation results and their associated uncertainties, subsequent T2 lesion detection performance is improved when evaluated on a proprietary large-scale, multi-site, clinical trial dataset acquired from patients with Multiple Sclerosis. (ii) We show an improvement in brain tumour segmentation performance when the uncertainty map associated with a synthesised missing MR volume is provided as an additional input to a follow-up brain tumour segmentation network, when evaluated on the publicly available BraTS-2018 dataset. (iii) We show that by propagating uncertainties from a voxel-level hippocampus segmentation task, the subsequent regression of the Alzheimer's disease clinical score is improved.

关键词： Task analysis Uncertainty Lesions Biomedical imaging Image segmentation Deep learning Magnetic resonance imaging Bayesian deep learning uncertainty brain tumour multiple sclerosis Alzheimer's segmentation detection synthesis classification

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Artificial-Intelligence based high precision Brachytherapy dose calculation

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RADIOTHERAPY AND ONCOLOGY 2023年 182卷 S229-S230页

作者： Quetin, S. Maleki, F. Enger, S. Abbasinejad McGill Univ Biol & Biomed Engn Montreal PQ Canada Montreal Inst Learning Algorithms MILA Montreal PQ Canada Lady Davis Inst Med Res Radiat Oncol Montreal PQ Canada Univ Calgary Comp Sci Calgary AB Canada McGill Univ Dept Oncol Med Phys Unit Montreal PQ Canada

OC-0294

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