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Global Cross-Entropy Loss for Deep Face Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Contemporary deep face recognition techniques predominantly utilize the Softmax loss function, designed based on the similarities between sample features and class prototypes. These similarities can be categorized into four types: in-sample target si...

Automated Autism Assessment With Multimodal Data and Ensemble Learning: A Scalable and Consistent Robot-Enhanced Therapy Framework.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Navigating the complexities of Autism Spectrum Disorder (ASD) diagnosis and intervention requires a nuanced approach that addresses both the inherent variability in therapeutic practices and the imperative for scalable solutions. This paper presents ...

VSR-Net: Vessel-Like Structure Rehabilitation Network With Graph Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
The morphologies of vessel-like structures, such as blood vessels and nerve fibres, play significant roles in disease diagnosis, e.g., Parkinson's disease. Although deep network-based refinement segmentation and topology-preserving segmentation metho...

Rethinking Copy-Paste for Consistency Learning in Medical Image Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Semi-supervised learning based on consistency learning offers significant promise for enhancing medical image segmentation. Current approaches use copy-paste as an effective data perturbation technique to facilitate weak-to-strong consistency learnin...

LAST-PAIN: Learning Adaptive Spike Thresholds for Low Back Pain Biosignals Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Spiking neural networks (SNNs) present the potential for ultra-low-power computation, especially when implemented on dedicated neuromorphic hardware. However, a significant challenge is the efficient conversion of continuous real-world data into the ...

The role of the dopamine system in autism spectrum disorder revealed using machine learning: an ABIDE database-based study.

Cerebral cortex (New York, N.Y. : 1991)
This study explores the diagnostic value of dopamine system imaging characteristics in children with autism spectrum disorder. Functional magnetic resonance data from 551 children in the Autism Brain Imaging Data Exchange database were analyzed, focu...

Interpretable machine learning unveils key predictors and default values in an expanded database of human in vitro dermal absorption studies with pesticides.

Regulatory toxicology and pharmacology : RTP
The skin is the main route of exposure to plant protection products for operators, workers, residents, and bystanders. Assessing dermal absorption is key for evaluating pesticide exposure. The initial approach to risk assessment involves using defaul...

Rethinking model prototyping through the MedMNIST+ dataset collection.

Scientific reports
The integration of deep learning based systems in clinical practice is often impeded by challenges rooted in limited and heterogeneous medical datasets. In addition, the field has increasingly prioritized marginal performance gains on a few, narrowly...

Contextual information contributes to biomedical named entity normalization.

Journal of biomedical informatics
OBJECTIVE: As one of the most crucial upstream tasks in biomedical informatics, biomedical named entity normalization (BNEN) aims to map mentioned named entities to uniform standard identifiers or terms. Most existing methods only consider the simila...

Enhancing Blood-Brain Barrier Penetration Prediction by Machine Learning-Based Integration of Novel and Existing, In Silico and Experimental Molecular Parameters from a Standardized Database.

Journal of chemical information and modeling
Predicting blood-brain barrier (BBB) penetration is crucial for developing central nervous system (CNS) drugs, representing a significant hurdle in successful clinical phase I studies. One of the most valuable properties for this prediction is the po...