AIMC Topic: Supervised Machine Learning

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A novel self-supervised graph clustering method with reliable semi-supervision.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
Cluster analysis, as a core technique in unsupervised learning, has widespread applications. With the increasing complexity of data, deep clustering, which integrates the advantages of deep learning and traditional clustering algorithms, demonstrates...
Humans Cluster Analysis Algorithms Neural Networks, Computer Supervised Machine Learning Deep Learning
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Semi-supervised non-negative matrix factorization with structure preserving for image clustering.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
Semi-supervised learning methods have wide applications thanks to the reasonable utilization for a part of label information of data. In recent years, non-negative matrix factorization (NMF) has received considerable attention because of its interpre...
Humans Neural Networks, Computer Algorithms Supervised Machine Learning Pattern Recognition, Automated Image Processing, Computer-Assisted Cluster Analysis
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Self-training EEG discrimination model with weakly supervised sample construction: An age-based perspective on ASD evaluation.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
Deep learning for Electroencephalography (EEG) has become dominant in the tasks of discrimination and evaluation of brain disorders. However, despite its significant successes, this approach has long been facing challenges due to the limited availabi...
Deep Learning Adolescent Age Factors Electroencephalography Child Humans Female Male Neural Networks, Computer Autism Spectrum Disorder Supervised Machine Learning
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Towards zero-shot human-object interaction detection via vision-language integration.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
Human-object interaction (HOI) detection aims to locate human-object pairs and identify their interaction categories in images. Most existing methods primarily focus on supervised learning, which relies on extensive manual HOI annotations. Such heavy...
Semantics Visual Perception Neural Networks, Computer Language Supervised Machine Learning Humans
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Self-Supervised Feature Detection and 3D Reconstruction for Real-Time Neuroendoscopic Guidance.

IEEE transactions on bio-medical engineering Jul 1, 2025
OBJECTIVE: Transventricular approach to deep-brain targets offers direct visualization but also imparts deformation that challenges accurate neuronavigation. 3D reconstruction and registration of the endoscopic view could provide up-to-date, real-tim...
Imaging, Three-Dimensional Supervised Machine Learning Neuroendoscopy Algorithms Brain Humans Neuronavigation
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Hyperspectral anomaly detection with self-supervised anomaly prior.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
Hyperspectral anomaly detection (HAD) can identify and locate the targets without any known information and is widely applied in Earth observation and military fields. The majority of existing HAD methods use the low-rank representation (LRR) model t...
Hyperspectral Imaging Humans Algorithms Neural Networks, Computer Supervised Machine Learning
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Self-Supervised Multimodal Learning: A Survey.

IEEE transactions on pattern analysis and machine intelligence Jul 1, 2025
Multimodal learning, which aims to understand and analyze information from multiple modalities, has achieved substantial progress in the supervised regime in recent years. However, the heavy dependence on data paired with expensive human annotations ...
Algorithms Image Processing, Computer-Assisted Humans Supervised Machine Learning
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Prediction of Specificity of α-Conotoxins to Subtypes of Human Nicotinic Acetylcholine Receptors with Semi-supervised Machine Learning.

ACS chemical neuroscience Jun 18, 2025
Conotoxins are a family of highly toxic neurotoxins composed of cysteine-rich peptides produced by marine cone snails. The most lethal cone snail species to humans is with fatality rates of up to ∼65% from a single sting, which is caused mostly by t...
Amino Acid Sequence Conotoxins Conus Snail Protein Binding Humans Supervised Machine Learning Receptors, Nicotinic Machine Learning
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Supervised and unsupervised learning for lung perfusion data segmentation in electrical impedance tomography.

Biomedical physics & engineering express Jun 13, 2025
: Effective lung gas exchange relies on the balance between alveolar ventilation and perfusion, which can be disrupted in mechanically ventilated patients. Lung perfusion assessment using electrical impedance tomography (EIT) typically involves a sud...
Image Processing, Computer-Assisted Support Vector Machine Unsupervised Machine Learning Lung Swine Electric Impedance Acute Lung Injury Tomography Supervised Machine Learning Perfusion Algorithms Animals Neural Networks, Computer
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Exploring interpretable echo analysis using self-supervised parcels.

Computers in biology and medicine Jun 1, 2025
The application of AI for predicting critical heart failure endpoints using echocardiography is a promising avenue to improve patient care and treatment planning. However, fully supervised training of deep learning models in medical imaging requires ...
Heart Failure Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Supervised Machine Learning Echocardiography Deep Learning Female Humans Male
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