AIMC Topic: Supervised Machine Learning

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Dynamic graph based weakly supervised deep hashing for whole slide image classification and retrieval.

Medical image analysis Jan 23, 2025
Recently, a multi-scale representation attention based deep multiple instance learning method has proposed to directly extract patch-level image features from gigapixel whole slide images (WSIs), and achieved promising performance on multiple popular...
Image Interpretation, Computer-Assisted Humans Algorithms Deep Learning Image Processing, Computer-Assisted Supervised Machine Learning
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Leveraging labelled data knowledge: A cooperative rectification learning network for semi-supervised 3D medical image segmentation.

Medical image analysis Jan 22, 2025
Semi-supervised 3D medical image segmentation aims to achieve accurate segmentation using few labelled data and numerous unlabelled data. The main challenge in the design of semi-supervised learning methods consists in the effective use of the unlabe...
Image Interpretation, Computer-Assisted Humans Algorithms Neural Networks, Computer Imaging, Three-Dimensional Supervised Machine Learning
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Self-supervised learning of scale-invariant neural representations of space and time.

Journal of computational neuroscience Jan 22, 2025
Hippocampal representations of space and time seem to share a common coding scheme characterized by neurons with bell-shaped tuning curves called place and time cells. The properties of the tuning curves are consistent with Weber's law, such that, in...
Learning Animals Models, Neurological Supervised Machine Learning Neurons Hippocampus Neural Networks, Computer Space Perception Time Perception
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PeptideForest: Semisupervised Machine Learning Integrating Multiple Search Engines for Peptide Identification.

Journal of proteome research Jan 22, 2025
The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, d...
Search Engine HEK293 Cells Escherichia coli Proteins Peptides Proteomics Proteome Machine Learning Humans Escherichia coli Software Supervised Machine Learning Algorithms Tandem Mass Spectrometry
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S2P-Matching: Self-Supervised Patch-Based Matching Using Transformer for Capsule Endoscopic Images Stitching.

IEEE transactions on bio-medical engineering Jan 21, 2025
The Magnetically Controlled Capsule Endoscopy (MCCE) has a limited shooting range, resulting in capturing numerous fragmented images and an inability to precisely locate and examine the region of interest (ROI) as traditional endoscopy can. Addressin...
Image Interpretation, Computer-Assisted Gastrointestinal Tract Capsule Endoscopy Humans Algorithms Supervised Machine Learning
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Development of metastasis and survival prediction model of luminal and non-luminal breast cancer with weakly supervised learning based on pathomics.

PeerJ Jan 21, 2025
OBJECTIVE: Breast cancer stands as the most prevalent form of cancer among women globally. This heterogeneous disease exhibits varying clinical behaviors. The stratification of breast cancer patients into risk groups, determined by their metastasis a...
Female Aged Adult Humans Prognosis Supervised Machine Learning Neoplasm Metastasis Breast Neoplasms Middle Aged Deep Learning
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Perfusion estimation from dynamic non-contrast computed tomography using self-supervised learning and a physics-inspired U-net transformer architecture.

International journal of computer assisted radiology and surgery Jan 20, 2025
PURPOSE: Pulmonary perfusion imaging is a key lung health indicator with clinical utility as a diagnostic and treatment planning tool. However, current nuclear medicine modalities face challenges like low spatial resolution and long acquisition times...
Perfusion Imaging Four-Dimensional Computed Tomography Female Male Middle Aged Lung Deep Learning Supervised Machine Learning Humans Tomography, X-Ray Computed
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DGMSCL: A dynamic graph mixed supervised contrastive learning approach for class imbalanced multivariate time series classification.

Neural networks : the official journal of the International Neural Network Society Jan 17, 2025
In the Imbalanced Multivariate Time Series Classification (ImMTSC) task, minority-class instances typically correspond to critical events, such as system faults in power grids or abnormal health occurrences in medical monitoring. Despite being rare a...
Supervised Machine Learning Multivariate Analysis Neural Networks, Computer Time Factors Humans
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Supervised learning approaches for predicting Ebola-Human Protein-Protein interactions.

Gene Jan 17, 2025
The goal of this research work is to predict protein-protein interactions (PPIs) between the Ebola virus and the host who is at risk of infection. Since there are very limited databases available on the Ebola virus; we have prepared a comprehensive d...
Humans Viral Proteins Protein Interaction Maps Protein Interaction Mapping Computational Biology Hemorrhagic Fever, Ebola Host-Pathogen Interactions Support Vector Machine Ebolavirus Databases, Protein Gene Ontology Supervised Machine Learning
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Relationship between lifestyle factors and cardiovascular disease prevalence in Somaliland: A supervised machine learning approach using data from Hargeisa Group Hospital, 2024.

Current problems in cardiology Jan 17, 2025
BACKGROUND: Cardiovascular diseases (CVDs) are leading contributors to global morbidity and mortality, with low- and middle-income countries experiencing disproportionately high burdens. In Somaliland, urbanization and lifestyle transitions have incr...
Female Risk Factors Aged Humans Male Cardiovascular Diseases Middle Aged Young Adult Adult Cross-Sectional Studies Supervised Machine Learning Life Style Somalia Risk Assessment Prevalence
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