@article{allaoui_ha-u3net_2025,

title = {HA-U3Net: A modality-agnostic framework for 3D medical image segmentation using nested V-Net structure and hybrid attention},

author = {M. L. Allaoui and M. S. Allili and A. Belaid},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011370963&doi=10.1016%2Fj.knosys.2025.114127&partnerID=40&md5=d98a109f015445adb3001bb4017bf953},

doi = {10.1016/j.knosys.2025.114127},

issn = {09507051 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Knowledge-Based Systems},

volume = {327},

abstract = {3D medical image segmentation is essential for disease diagnosis and treatment planning across a wide range of imaging modalities (e.g., MRI, CT, ultrasound, and PET). However, modality-specific challenges, such as noise, artifacts, low contrast, and anatomical variability, along with the presence of small lesions and fuzzy boundaries, hinder the generalization capability of existing segmentation models. In this work, we present HA-U3Net, a novel 3D U-Net-based model designed to address these limitations through a stepwise approach. First, we introduce a deeply nested U3-shaped structure built upon 3D V-Net modules, enabling multi-scale hierarchical representation learning. Second, we integrate a hybrid attention mechanism combining spatial and channel-wise attention to enhance salient features extraction and the delineation of small or poorly defined structures. Third, we demonstrate the cross-modality generalization capabilities of HA-U3Net through extensive evaluations on several datasets, where our model consistently outperforms baseline methods. Finally, we propose a lightweight variant, U3Mamba, reducing computational complexity while maintaining high performance. © 2025 Elsevier B.V.},

keywords = {3D medical image, 3D medical image segmentation, Diagnosis, Diagnosis planning, Disease diagnosis, Disease treatment, Generalization capability, Image segmentation, Magnetic resonance imaging, Medical image processing, Medical image segmentation, Nested volume-structure, Net structures, Self hybrid attention, Structures (built objects)},

pubstate = {published},

tppubtype = {article}

}

@article{berube_childhood_2025,

title = {Childhood maltreatment influences parental mimicry of children's emotional facial expressions},

author = {A. Bérubé and R. Pétrin and M. Boudreault and G. Marcotte-Beaumier and C. Blais},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105020986193&doi=10.1016%2Fj.chiabu.2025.107787&partnerID=40&md5=18593b82f701fc76ad054419d48dfc69},

doi = {10.1016/j.chiabu.2025.107787},

issn = {01452134 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Child Abuse and Neglect},

volume = {170},

abstract = {Background: Childhood maltreatment can disrupt socio-emotional functioning, potentially influencing how parents respond to children's emotional facial expressions. Mimicry, an automatic reaction to others' facial expressions, is a critical mechanism for social bonding and affiliation in parent-child relationships. However, the effects of childhood maltreatment on parental mimicry remain underexplored. Objective: This study examined the relationship between different forms of childhood maltreatment and parents' mimicry of children's emotional facial expressions. Participants and setting: Fifty-seven parents participated in an emotion recognition task conducted either at a local community organization or at the university laboratory. Methods: Parents' facial reactions were recorded and analyzed using FaceReader software to detect mimicry. The Childhood Trauma Questionnaire (CTQ) assessed parental history of maltreatment. A path analysis model was conducted to evaluate the associations between forms of childhood maltreatment and parental mimicry. Results: A history of physical abuse predicted increased expressions of anger, while physical neglect was linked to reduced anger but heightened disgust. Emotional and sexual abuse were associated with diminished mimicry of sadness, whereas emotional neglect predicted enhanced sadness mimicry. Conclusions: Findings suggest that childhood maltreatment alters parents' facial reactions to children's emotional facial expressions, potentially impacting parental sensitivity. © 2025},

keywords = {Adolescent, adult, Adult Survivors of Child Abuse, anger, article, Child, Child Abuse, child abuse survivor, child parent relation, childhood maltreatment, Childhood Trauma Questionnaire, controlled study, Diagnosis, disgust, emotion, Emotional facial expressions, emotional neglect, Emotions, Facial Expression, female, human, Humans, major clinical study, male, Mimicry, neglect, parent, Parent-Child Relations, Parenting, Parents, path analysis, physical abuse, psychological functioning, psychology, questionnaire, sadness, sexual abuse, social bonding, Surveys and Questionnaires, Young Adult},

pubstate = {published},

tppubtype = {article}

}

@article{allaoui_mixlvmm_2025,

title = {MixLVMM: A Mixture of Lightweight Vision Mamba Model for Enhancing Skin Lesion Segmentation Across High Tone Variability},

author = {M. L. Allaoui and M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105012036322&doi=10.1109%2FACCESS.2025.3588476&partnerID=40&md5=1cf51dcf43653e1677ad36a1360392ac},

doi = {10.1109/ACCESS.2025.3588476},

issn = {21693536 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Access},

volume = {13},

pages = {121234–121249},

abstract = {Accurate skin lesion segmentation remains a critical challenge in automated dermatological diagnosis due to heterogeneous lesion presentations, ambiguous boundaries, imaging artifacts, and significant variability in skin and lesion tones across diverse populations. Current segmentation methods inadequately address these multifaceted complexities, particularly failing to handle extreme tone variations that can lead to diagnostic bias. To address these limitations, we present the Mixture of Lightweight Vision Mamba Model (MixLVMM), a novel expert-based framework that enhances segmentation performance across high tone variability through specialized processing. Our approach employs a Siamese network with triplet loss as a gate mechanism to categorize lesions based on tonal characteristics, routing each image to specialized Vision Mamba Model (VMM) experts optimized for specific lesion categories. Each expert utilizes a U-shaped architecture incorporating Focused Vision Mamba blocks and Adaptive Salient Region Attention modules to capture lesion-specific features while maintaining computational efficiency. Comprehensive evaluation on ISIC and PH2 datasets demonstrates that MixLVMM achieves superior segmentation performance with an average Dice coefficient of 93%, surpassing state-of-the-art methods while maintaining efficiency with only 2.5M parameters. These results establish MixLVMM as a robust solution for addressing tone-related segmentation challenges in clinical dermatology, offering both high accuracy and practical deployment feasibility for real-world applications. © 2013 IEEE.},

keywords = {Attention mechanism, Attention mechanisms, Computational efficiency, Critical challenges, Dermatology, Diagnosis, Image segmentation, Lesion segmentations, Lung cancer, Mixture of experts model, Mixture-of-experts model, Segmentation performance, Skin lesion, Skin lesion segmentation, Skin/lesion tone variability, Vision mamba},

pubstate = {published},

tppubtype = {article}

}

@article{berubeChildhoodMaltreatmentInfluences2025,

title = {Childhood maltreatment influences parental mimicry of children's emotional facial expressions},

author = {A. Bérubé and R. Pétrin and M. Boudreault and G. Marcotte-Beaumier and C. Blais},

url = {https://www.scopus.com/pages/publications/105020986193?origin=resultslist},

doi = {10.1016/j.chiabu.2025.107787},

issn = {01452134 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Child Abuse and Neglect},

volume = {170},

publisher = {Elsevier Ltd},

abstract = {Background: Childhood maltreatment can disrupt socio-emotional functioning, potentially influencing how parents respond to children's emotional facial expressions. Mimicry, an automatic reaction to others' facial expressions, is a critical mechanism for social bonding and affiliation in parent-child relationships. However, the effects of childhood maltreatment on parental mimicry remain underexplored. Objective: This study examined the relationship between different forms of childhood maltreatment and parents' mimicry of children's emotional facial expressions. Participants and setting: Fifty-seven parents participated in an emotion recognition task conducted either at a local community organization or at the university laboratory. Methods: Parents' facial reactions were recorded and analyzed using FaceReader software to detect mimicry. The Childhood Trauma Questionnaire (CTQ) assessed parental history of maltreatment. A path analysis model was conducted to evaluate the associations between forms of childhood maltreatment and parental mimicry. Results: A history of physical abuse predicted increased expressions of anger, while physical neglect was linked to reduced anger but heightened disgust. Emotional and sexual abuse were associated with diminished mimicry of sadness, whereas emotional neglect predicted enhanced sadness mimicry. Conclusions: Findings suggest that childhood maltreatment alters parents' facial reactions to children's emotional facial expressions, potentially impacting parental sensitivity. © 2025},

keywords = {Adolescent, adult, Adult Survivors of Child Abuse, anger, article, Child, Child Abuse, child abuse survivor, child parent relation, childhood maltreatment, Childhood Trauma Questionnaire, controlled study, Diagnosis, disgust, emotion, Emotional facial expressions, emotional neglect, Emotions, Facial Expression, female, human, Humans, major clinical study, male, Mimicry, neglect, parent, Parent-Child Relations, Parenting, Parents, path analysis, physical abuse, psychological functioning, psychology, questionnaire, sadness, sexual abuse, social bonding, Surveys and Questionnaires, Young Adult},

pubstate = {published},

tppubtype = {article}

}

@article{moudoudAdvancingPrivacyFairness2025,

title = {Advancing Privacy and Fairness in Healthcare Using Federated Edge Learning and Blockchain},

author = {H. Moudoud and Z. Abou El Houda and B. Brik},

url = {https://www.scopus.com/pages/publications/105012264671?origin=resultslist},

doi = {10.1109/JIOT.2025.3589179},

issn = {23274662 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Internet of Things Journal},

volume = {12},

number = {22},

pages = {46129–46137},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {artificial intelligence (AI) has revolutionized many fields, including healthcare. The adoption of AI techniques in critical healthcare tasks, such as cancer diagnosis, holds great promise for revolutionizing the healthcare system. AI algorithms can be trained on vast datasets to recognize patterns, detect anomalies, and provide accurate assessments. However, the lack of realistic and up-to-date medical data poses a significant challenge to the widespread adoption of AI techniques. Additionally, privacy concerns surrounding sensitive medical data, particularly patient health records (PHRs), hinder data sharing among healthcare practitioners. This article aims to address these challenges by proposing a novel framework, entitled SecureMed, that uses federated learning (FL) and Blockchain to preserve privacy in the healthcare system. In particular, SecureMed consists of: 1) a novel distributed architecture that enables secure collaboration among multiple mobile edge computing (MEC)-based Internet of Medical Things (IoMT) devices, while ensuring the privacy of healthcare systems; 2) a fairness-aware FL solution to ensure that model performance is balanced across all participating healthcare institutions, addressing the issue of imbalanced data contributions; 3) a secure multiparty computation (SMPC) protocol to ensure secure aggregation of local model updates; and 4) a blockchain-based reputation model for collaborative FL training. The proposed framework leverages smart contracts to ensure trustworthiness, decentralization, and transparency in the FL process. The experimental results using the CIC IoMT dataset 2024 highlight the promising potential of SecureMed in revolutionizing healthcare systems. © 2014 IEEE.},

keywords = {Artificial intelligence algorithms, Artificial intelligence techniques, Block-chain, Blockchain, cancer diagnosis, Diagnosis, Distributed computer systems, Federated edge learning, Health care, Healthcare, Healthcare systems, Internet of medical thing, Internet of Medical Things (IoMT), Learning systems, Medical computing, Medical data, Network security, Privacy, Sensitive data},

pubstate = {published},

tppubtype = {article}

}

@article{allaouiHAU3NetModalityagnosticFramework2025,

title = {HA-U3Net: A modality-agnostic framework for 3D medical image segmentation using nested V-Net structure and hybrid attention},

author = {M. L. Allaoui and M. S. Allili and A. Belaid},

url = {https://www.scopus.com/pages/publications/105011370963?origin=resultslist},

doi = {10.1016/j.knosys.2025.114127},

issn = {09507051 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Knowledge-Based Systems},

volume = {327},

publisher = {Elsevier B.V.},

abstract = {3D medical image segmentation is essential for disease diagnosis and treatment planning across a wide range of imaging modalities (e.g., MRI, CT, ultrasound, and PET). However, modality-specific challenges, such as noise, artifacts, low contrast, and anatomical variability, along with the presence of small lesions and fuzzy boundaries, hinder the generalization capability of existing segmentation models. In this work, we present HA-U3Net, a novel 3D U-Net-based model designed to address these limitations through a stepwise approach. First, we introduce a deeply nested U3-shaped structure built upon 3D V-Net modules, enabling multi-scale hierarchical representation learning. Second, we integrate a hybrid attention mechanism combining spatial and channel-wise attention to enhance salient features extraction and the delineation of small or poorly defined structures. Third, we demonstrate the cross-modality generalization capabilities of HA-U3Net through extensive evaluations on several datasets, where our model consistently outperforms baseline methods. Finally, we propose a lightweight variant, U3Mamba, reducing computational complexity while maintaining high performance. © 2025 Elsevier B.V.},

keywords = {3D medical image, 3D medical image segmentation, Diagnosis, Diagnosis planning, Disease diagnosis, Disease treatment, Generalization capability, Image segmentation, Magnetic resonance imaging, Medical image processing, Medical image segmentation, Nested volume-structure, Net structures, Self hybrid attention, Structures (built objects)},

pubstate = {published},

tppubtype = {article}

}

@article{allaouiMixLVMMMixtureLightweight2025,

title = {MixLVMM: A Mixture of Lightweight Vision Mamba Model for Enhancing Skin Lesion Segmentation Across High Tone Variability},

author = {M. L. Allaoui and M. S. Allili},

url = {https://www.scopus.com/pages/publications/105012036322?origin=resultslist},

doi = {10.1109/ACCESS.2025.3588476},

issn = {21693536 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Access},

volume = {13},

pages = {121234–121249},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Accurate skin lesion segmentation remains a critical challenge in automated dermatological diagnosis due to heterogeneous lesion presentations, ambiguous boundaries, imaging artifacts, and significant variability in skin and lesion tones across diverse populations. Current segmentation methods inadequately address these multifaceted complexities, particularly failing to handle extreme tone variations that can lead to diagnostic bias. To address these limitations, we present the Mixture of Lightweight Vision Mamba Model (MixLVMM), a novel expert-based framework that enhances segmentation performance across high tone variability through specialized processing. Our approach employs a Siamese network with triplet loss as a gate mechanism to categorize lesions based on tonal characteristics, routing each image to specialized Vision Mamba Model (VMM) experts optimized for specific lesion categories. Each expert utilizes a U-shaped architecture incorporating Focused Vision Mamba blocks and Adaptive Salient Region Attention modules to capture lesion-specific features while maintaining computational efficiency. Comprehensive evaluation on ISIC and PH2 datasets demonstrates that MixLVMM achieves superior segmentation performance with an average Dice coefficient of 93%, surpassing state-of-the-art methods while maintaining efficiency with only 2.5M parameters. These results establish MixLVMM as a robust solution for addressing tone-related segmentation challenges in clinical dermatology, offering both high accuracy and practical deployment feasibility for real-world applications. © 2013 IEEE.},

keywords = {Attention mechanism, Attention mechanisms, Computational efficiency, Critical challenges, Dermatology, Diagnosis, Image segmentation, Lesion segmentations, Lung cancer, Mixture of experts model, Mixture-of-experts model, Segmentation performance, Skin lesion, Skin lesion segmentation, Skin/lesion tone variability, Vision mamba},

pubstate = {published},

tppubtype = {article}

}

@article{zhuWavRxDiseaseAgnosticGeneralizable2025,

title = {WavRx: A Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model},

author = {Y. Zhu and T. Falk},

url = {https://www.scopus.com/pages/publications/85203439930?origin=resultslist},

doi = {10.1109/JBHI.2024.3454550},

issn = {21682194 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {29},

number = {9},

pages = {6353–6365},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Speech is known to carry health-related attributes, which has emerged as a novel venue for remote and long-term health monitoring. However, existing models are usually tailored for a specific type of disease, and have been shown to lack generalizability across datasets. Furthermore, concerns have been raised recently towards the leakage of speaker identity from health embeddings. To mitigate these limitations, we propose WavRx, a speech health diagnostics model that captures the respiration and articulation related dynamics from a universal speech representation. Our in-domain and cross-domain experiments on six pathological speech datasets demonstrate WavRx as a new state-of-the-art health diagnostic model. Furthermore, we show that the amount of speaker identity entailed in the WavRx health embeddings is significantly reduced without extra guidance during training. An in-depth analysis of the model was performed, thus providing physiological interpretation of its improved generalizability and privacy-preserving ability. © 2013 IEEE.},

keywords = {Agnostic, area under the curve, article, artificial neural network, asthma, autoencoder, Benchmarking, breathing, chronic obstructive lung disease, Computer-Assisted, controlled study, convolutional neural network, coronavirus disease 2019, Cross-domain, Databases, Diagnosis, Diagnostic, Diagnostic model, diagnostic test accuracy study, diagnostics, Differential privacy, Dynamics, dysarthria, Electronic health record, embedding, Embeddings, Factual, factual database, Generalizability, Health embedding, Health embeddings, Health monitoring, human, Humans, Machine learning, malignant neoplasm, model, Pathological speech, pathophysiology, physiology, pneumonia, Privacy, Privacy preserving, privacy preserving speech health diagnostic model, privacy-preserving, Privacy-preserving techniques, receiver operating characteristic, short time Fourier transform, Signal processing, speech, speech articulation, speech disorder, Speech Disorders, State of the art, temporal representation encoder, training, waveform},

pubstate = {published},

tppubtype = {article}

}

@article{berubeChildhoodMaltreatmentInfluences2025a,

title = {Childhood maltreatment influences parental mimicry of children's emotional facial expressions},

author = {A. Bérubé and R. Pétrin and M. Boudreault and G. Marcotte-Beaumier and C. Blais},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105020986193&doi=10.1016%2Fj.chiabu.2025.107787&partnerID=40&md5=18593b82f701fc76ad054419d48dfc69},

doi = {10.1016/j.chiabu.2025.107787},

issn = {01452134 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Child Abuse and Neglect},

volume = {170},

abstract = {Background: Childhood maltreatment can disrupt socio-emotional functioning, potentially influencing how parents respond to children's emotional facial expressions. Mimicry, an automatic reaction to others' facial expressions, is a critical mechanism for social bonding and affiliation in parent-child relationships. However, the effects of childhood maltreatment on parental mimicry remain underexplored. Objective: This study examined the relationship between different forms of childhood maltreatment and parents' mimicry of children's emotional facial expressions. Participants and setting: Fifty-seven parents participated in an emotion recognition task conducted either at a local community organization or at the university laboratory. Methods: Parents' facial reactions were recorded and analyzed using FaceReader software to detect mimicry. The Childhood Trauma Questionnaire (CTQ) assessed parental history of maltreatment. A path analysis model was conducted to evaluate the associations between forms of childhood maltreatment and parental mimicry. Results: A history of physical abuse predicted increased expressions of anger, while physical neglect was linked to reduced anger but heightened disgust. Emotional and sexual abuse were associated with diminished mimicry of sadness, whereas emotional neglect predicted enhanced sadness mimicry. Conclusions: Findings suggest that childhood maltreatment alters parents' facial reactions to children's emotional facial expressions, potentially impacting parental sensitivity. © 2025},

keywords = {Adolescent, adult, Adult Survivors of Child Abuse, anger, article, Child, Child Abuse, child abuse survivor, child parent relation, childhood maltreatment, Childhood Trauma Questionnaire, controlled study, Diagnosis, disgust, emotion, Emotional facial expressions, emotional neglect, Emotions, Facial Expression, female, human, Humans, major clinical study, male, Mimicry, neglect, parent, Parent-Child Relations, Parenting, Parents, path analysis, physical abuse, psychological functioning, psychology, questionnaire, sadness, sexual abuse, social bonding, Surveys and Questionnaires, Young Adult},

pubstate = {published},

tppubtype = {article}

}