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

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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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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learning and fine-tuning a generic value-selection heuristic inside a constraint programming solver

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CONSTRAINTS 2024年第3-4期29卷 234-260页

作者： Marty, Tom Boisvert, Leo Francois, Tristan Tessier, Pierre Gautier, Louis Rousseau, Louis-Martin Cappart, Quentin Polytech Montreal Montreal PQ Canada Ecole Polytech Palaiseau France Quebec Inst Learning Algorithms MILA Montreal PQ Canada

Constraint programming is known for being an efficient approach to solving combinatorial problems. Important design choices in a solver are the branching heuristics, designed to lead the search to the best solutions in a minimum amount of time. However, developing these heuristics is a time-consuming process that requires problem-specific expertise. This observation has motivated many efforts to use machine learning to automatically learn efficient heuristics without expert intervention. Although several generic variable-selection heuristics are available in the literature, the options for value-selection heuristics are more scarce. We propose to tackle this issue by introducing a generic learning procedure that can be used to obtain a value-selection heuristic inside a constraint programming solver. This has been achieved thanks to the combination of a deep Q-learning algorithm, a tailored reward signal, and a heterogeneous graph neural network. Experiments on graph coloring, maximum independent set, maximum cut, and minimum vertex cover problems show that this framework competes with the well-known impact-based and activity-based search heuristics and can find solutions close to optimality without requiring a large number of backtracks. Additionally, we observe that fine-tuning a model with a different problem class can accelerate the learning process.

关键词： Constraint programming Branching heuristics Reinforcement learning

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Conditional Generation of Medical Images via Disentangled Adversarial Inference 1st

Conditional Generation of Medical Images via Disentangled Ad...

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1st Workshop on Deep Generative Models for Medical Image Computing and Computer Assisted Intervention (DGM4MICCAI) / 1st MICCAI Workshop on Data Augmentation, Labelling, and Imperfections (DALI)

作者： Havaei, Mohammad Mao, Ximeng Wang, Yipping Lao, Qicheng Imagia Montreal PQ Canada Univ Montreal Montreal Inst Learning Algorithms MILA Montreal PQ Canada

ISBN: (纸本)9783030882105;9783030882099

We propose DRAI-a dual adversarial inference framework with augmented disentanglement constraints-to learn from the image itself, disentangled representations of style and content, and use this information to impose control over conditional generation process. We undergo two novel regularization steps to ensure content-style disentanglement. First, we minimize the shared information between content and style by introducing a novel application of the gradient reverse layer (GRL);second, we introduce a self-supervised regularization method to further separate information in the content and style variables. We conduct extensive qualitative and quantitative assessments on two publicly available medical imaging datasets (LIDC and HAM10000) and test for conditional image generation and style-content disentanglement. We also show that our proposed model (DRAI) achieves the best disentanglement score and has the best overall performance.

关键词： Medical imaging

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Interpolation consistency training for semi-supervised learning

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NEURAL NETWORKS 2022年 145卷 90-106页

作者： Verma, Vikas Kawaguchi, Kenji Lamb, Alex Kannala, Juho Solin, Arno Bengio, Yoshua Lopez-Paz, David Montreal Inst Learning Algorithms MILA Montreal PQ Canada Aalto Univ Espoo Finland Harvard Univ Cambridge MA 02138 USA Facebook AI Res Paris France

We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. ICT encourages the prediction at an interpolation of unlabeled points to be consistent with the interpolation of the predictions at those points. In classification problems, ICT moves the decision boundary to low-density regions of the data distribution. Our experiments show that ICT achieves state-of-the-art performance when applied to standard neural network architectures on the CIFAR-10 and SVHN benchmark datasets. Our theoretical analysis shows that ICT corresponds to a certain type of data-adaptive regularization with unlabeled points which reduces overfitting to labeled points under high confidence values. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Semi-supervised learning Deep Neural Networks Mixup Consistency regularization

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Post-Editing Extractive Summaries by Definiteness Prediction

Post-Editing Extractive Summaries by Definiteness Prediction

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Meeting of the Association-for-Computational-Linguistics (ACL-EMNLP)

作者： Kabbara, Jad Cheung, Jackie Chi Kit McGill Univ Sch Comp Sci Montreal PQ Canada Montreal Inst Learning Algorithms Mila Montreal PQ Canada

ISBN: (纸本)9781955917100

Extractive summarization has been the mainstay of automatic summarization for decades. Despite all the progress, extractive summarizers still suffer from shortcomings including coreference issues arising from extracting sentences away from their original context in the source document. This affects the coherence and readability of extractive summaries. In this work, we propose a lightweight postediting step for extractive summaries that centers around a single linguistic decision: the definiteness of noun phrases. We conduct human evaluation studies that show that human expert judges substantially prefer the output of our proposed system over the original summaries. Moreover, based on an automatic evaluation study, we provide evidence for our system's ability to generate linguistic decisions that lead to improved extractive summaries. We also draw insights about how the automatic system is exploiting some local cues related to the writing style of the main article texts or summary texts to make the decisions, rather than reasoning about the contexts pragmatically.

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Augmented CycleGAN: learning Many-to-Many Mappings from Unpaired Data 35

Augmented CycleGAN: Learning Many-to-Many Mappings from Unpa...

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35th International Conference on Machine learning (ICML)

作者： Almahairi, Amjad Rajeswar, Sai Sordoni, Alessandro Bachman, Philip Courville, Aaron Montreal Inst Learning Algorithms MILA Montreal PQ Canada Microsoft Res Montreal Montreal PQ Canada MSR Montreal Montreal PQ Canada

ISBN: (纸本)9781510867963

learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by reducing the need for paired data. CycleGAN was recently proposed for this problem, but critically assumes the underlying inter-domain mapping is approximately deterministic and one-to-one. This assumption renders the model ineffective for tasks requiring flexible, many-to-many mappings. We propose a new model, called Augmented CycleGAN, which learns many-to-many mappings between domains. We examine Augmented CycleGAN qualitatively and quantitatively on several image datasets.

关键词： Image segmentation

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Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2022年第6期33卷 2350-2364页

作者： Macedo, David Ren, Tsang Ing Zanchettin, Cleber Oliveira, Adriano L. I. Ludermir, Teresa Univ Montreal UdeM Montreal Inst Learning Algorithms MILA Montreal PQ H3T 1J4 Canada Univ Fed Pernambuco Ctr Informat BR-50670901 Recife PE Brazil

In this article, we argue that the unsatisfactory out-of-distribution (OOD) detection performance of neural networks is mainly due to the SoftMax loss anisotropy and propensity to produce low entropy probability distributions in disagreement with the principle of maximum entropy. On the one hand, current OOD detection approaches usually do not directly fix the SoftMax loss drawbacks, but rather build techniques to circumvent it. Unfortunately, those methods usually produce undesired side effects (e.g., classification accuracy drop, additional hyperparameters, slower inferences, and collecting extra data). On the other hand, we propose replacing SoftMax loss with a novel loss function that does not suffer from the mentioned weaknesses. The proposed IsoMax loss is isotropic (exclusively distance-based) and provides high entropy posterior probability distributions. Replacing the SoftMax loss by IsoMax loss requires no model or training changes. Additionally, the models trained with IsoMax loss produce as fast and energy-efficient inferences as those trained using SoftMax loss. Moreover, no classification accuracy drop is observed. The proposed method does not rely on outlier/background data, hyperparameter tuning, temperature calibration, feature extraction, metric learning, adversarial training, ensemble procedures, or generative models. Our experiments showed that IsoMax loss works as a seamless SoftMax loss drop-in replacement that significantly improves neural networks' OOD detection performance. Hence, it may be used as a baseline OOD detection approach to be combined with current or future OOD detection techniques to achieve even higher results.

关键词： Entropy Training Neural networks Feature extraction Data preprocessing Measurement Prototypes Entropic score isotropy maximization loss maximum entropy principle out-of-distribution (OOD) detection

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Behavioural equivalences for continuous-time Markov processes

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MATHEMATICAL STRUCTURES IN COMPUTER SCIENCE 2023年第4-5期33卷 222-258页

作者： Chen, Linan Clerc, Florence Panangaden, Prakash McGill Univ Dept Math & Stat Montreal PQ Canada Sch Comp Sci McGil lUnivers DEEL Quebec Montreal PQ Canada McGill Univ Montreal Inst Learning Algorithms MILA Sch Comp Sci Montreal PQ Canada

Bisimulation is a concept that captures behavioural equivalence of states in a variety of types of transition systems. It has been widely studied in a discrete-time setting. The core of this work is to generalise the discrete-time picture to continuous time by providing a notion of behavioural equivalence for continuous-time Markov processes. In Chen et al. [(2019). Electronic Notes in Theoretical Computer Science 347 45-63.], we proposed two equivalent definitions of bisimulation for continuous-time stochastic processes where the evolution is a flow through time: the first one as an equivalence relation and the second one as a cospan of morphisms. In Chen et al. [(2020). Electronic Notes in Theoretical Computer Science.], we developed the theory further: we introduced different concepts that correspond to different behavioural equivalences and compared them to bisimulation. In particular, we studied the relation between bisimulation and symmetry groups of the dynamics. We also provided a game interpretation for two of the behavioural equivalences. The present work unifies the cited conference presentations and gives detailed proofs.

关键词： Stochastic processes Markov processes continuous time bisimulation diffusion

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Interpolated Adversarial Training: Achieving robust neural networks without sacrificing too much accuracy

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NEURAL NETWORKS 2022年第0期154卷 218-233页

作者： Lamb, Alex Verma, Vikas Kawaguchi, Kenji Matyasko, Alexander Khosla, Savya Kannala, Juho Bengio, Yoshua Montreal Inst Learning Algorithms MILA Montreal PQ Canada Aalto Univ Espoo Finland Harvard Univ Cambridge MA 02138 USA Natl Univ Singapore Singapore Singapore Google New Delhi India

Adversarial robustness has become a central goal in deep learning, both in the theory and the practice. However, successful methods to improve the adversarial robustness (such as adversarial training) greatly hurt generalization performance on the unperturbed data. This could have a major impact on how the adversarial robustness affects real world systems (i.e. many may opt to forego robustness if it can improve accuracy on the unperturbed data). We propose Interpolated Adversarial Training, which employs recently proposed interpolation based training methods in the framework of adversarial training. On CIFAR-10, adversarial training increases the standard test error ( when there is no adversary) from 4.43% to 12.32%, whereas with our Interpolated adversarial training we retain the adversarial robustness while achieving a standard test error of only 6.45%. With our technique, the relative increase in the standard error for the robust model is reduced from 178.1% to just 45.5%. Moreover, we provide mathematical analysis of Interpolated Adversarial Training to confirm its efficiencies and demonstrate its advantages in terms of robustness and generalization. (C) 2022 The Authors. Published by Elsevier Ltd.

关键词： Adversarial robustness Mixup Manifold Mixup Standard test error

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