AIMC Topic: Supervised Machine Learning

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Assessment of anemia recovery using peripheral blood smears by deep semi-supervised learning.

Annals of hematology Feb 21, 2025
Monitoring anemia recovery is crucial for clinical intervention. Morphological assessment of red blood cells (RBCs) with peripheral blood smears (PBSs) provides additional information beyond routine blood tests. However, the PBS test is labor-intensi...
Erythrocyte Count Anemia Erythrocytes Mice Supervised Machine Learning Humans Male Deep Learning Animals
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Open-world semi-supervised relation extraction.

Neural networks : the official journal of the International Neural Network Society Feb 20, 2025
Semi-supervised Relation Extraction methods play an important role in extracting relationships from unstructured text, which can leverage both labeled and unlabeled data to improve extraction accuracy. However, these methods are grounded under the cl...
Semantics Natural Language Processing Supervised Machine Learning Data Mining Humans Neural Networks, Computer
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Temporal and spatial self supervised learning methods for electrocardiograms.

Scientific reports Feb 19, 2025
The limited availability of labeled ECG data restricts the application of supervised deep learning methods in ECG detection. Although existing self-supervised learning approaches have been applied to ECG analysis, they are predominantly image-based, ...
Supervised Machine Learning Electrocardiography Signal Processing, Computer-Assisted Humans Databases, Factual Deep Learning Algorithms
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Label-efficient sequential model-based weakly supervised intracranial hemorrhage segmentation in low-data non-contrast CT imaging.

Medical physics Feb 17, 2025
BACKGROUND: In clinical settings, intracranial hemorrhages (ICH) are routinely diagnosed using non-contrast CT (NCCT) in emergency stroke imaging for severity assessment. However, compared to magnetic resonance imaging (MRI), ICH shows low contrast a...
Humans Tomography, X-Ray Computed Intracranial Hemorrhages Image Processing, Computer-Assisted Supervised Machine Learning
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Supervised machine learning compared to large language models for identifying functional seizures from medical records.

Epilepsia Feb 17, 2025
OBJECTIVE: The Functional Seizures Likelihood Score (FSLS) is a supervised machine learning-based diagnostic score that was developed to differentiate functional seizures (FS) from epileptic seizures (ES). In contrast to this targeted approach, large...
Seizures Young Adult ROC Curve Adult Supervised Machine Learning Language Humans Electroencephalography Male Middle Aged Large Language Models Female Machine Learning Adolescent
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Mixed-Supervised Learning for Cell Classification.

Sensors (Basel, Switzerland) Feb 16, 2025
Cell classification based on histopathology images is crucial for tumor recognition and cancer diagnosis. Using deep learning, classification accuracy is hugely improved. Semi-supervised learning is an advanced deep learning approach that uses both l...
Algorithms Neoplasms Humans Image Processing, Computer-Assisted Deep Learning Supervised Machine Learning
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Rethinking Copy-Paste for Consistency Learning in Medical Image Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Feb 14, 2025
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...
Prostate Supervised Machine Learning Algorithms Humans Databases, Factual Image Processing, Computer-Assisted Machine Learning Male Diagnostic Imaging
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Active learning and margin strategies for arrhythmia classification in implantable devices.

Computers in biology and medicine Feb 13, 2025
BACKGROUND AND OBJECTIVES: The massive storage of cardiac arrhythmic episodes from Implantable Cardioverter Defibrillators (ICD) and the advent of new artificial intelligence algorithms are opening up new opportunities for electrophysiological knowle...
Humans Machine Learning Arrhythmias, Cardiac Algorithms Defibrillators, Implantable Supervised Machine Learning
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Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.

Science advances Feb 12, 2025
Machine learning (ML) is changing the world of computational protein design, with data-driven methods surpassing biophysical-based methods in experimental success. However, they are most often reported as case studies, lack integration and standardiz...
Protein Engineering Mutation Supervised Machine Learning Software Algorithms Machine Learning Proteins Computational Biology Models, Molecular
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Supervised Contrastive Learning Framework and Hardware Implementation of Learned ResNet for Real-Time Respiratory Sound Classification.

IEEE transactions on biomedical circuits and systems Feb 11, 2025
This paper presents a supervised contrastive learning (SCL) framework for respiratory sound classification and the hardware implementation of learned ResNet on field programmable gate array (FPGA) for real-time monitoring. At the algorithmic level, m...
Signal Processing, Computer-Assisted Humans Supervised Machine Learning Neural Networks, Computer Algorithms Respiratory Sounds Bayes Theorem
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