AIMC Topic: Supervised Machine Learning

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Semi-Supervised Learning Allows for Improved Segmentation With Reduced Annotations of Brain Metastases Using Multicenter MRI Data.

Journal of magnetic resonance imaging : JMRI Jan 10, 2025
BACKGROUND: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised learning offers potential efficient methods to improve model performance without excessive annotation ...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Middle Aged Deep Learning Supervised Machine Learning Image Interpretation, Computer-Assisted Retrospective Studies Female Brain Neoplasms Male Reproducibility of Results Algorithms Brain Humans
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The KMeansGraphMIL Model: A Weakly Supervised Multiple Instance Learning Model for Predicting Colorectal Cancer Tumor Mutational Burden.

The American journal of pathology Jan 10, 2025
Colorectal cancer (CRC) is one of the top three most lethal malignancies worldwide, posing a significant threat to human health. Recently proposed immunotherapy checkpoint blockade treatments have proven effective for CRC, but their use depends on me...
Humans Biomarkers, Tumor ROC Curve Colorectal Neoplasms Multiple-Instance Learning Algorithms Mutation Supervised Machine Learning
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DP-CLAM: A weakly supervised benign-malignant classification study based on dual-angle scanning ultrasound images of thyroid nodules.

Medical engineering & physics Jan 10, 2025
In this paper, a two-stage task weakly supervised learning algorithm is proposed. It accurately achieved patient-level classification task of benign and malignant thyroid nodules based on ultrasound images from two scanning angles: long axis and shor...
Image Processing, Computer-Assisted Ultrasonography Supervised Machine Learning Humans Thyroid Nodule Algorithms
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A hybrid unsupervised machine learning model with spectral clustering and semi-supervised support vector machine for credit risk assessment.

PloS one Jan 10, 2025
In credit risk assessment, unsupervised classification techniques can be introduced to reduce human resource expenses and expedite decision-making. Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performanc...
Supervised Machine Learning Algorithms Risk Assessment Humans Cluster Analysis Support Vector Machine Unsupervised Machine Learning
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Disentangled Active Learning on Graphs.

Neural networks : the official journal of the International Neural Network Society Jan 9, 2025
Active learning on graphs (ALG) has emerged as a compelling research field due to its capacity to address the challenge of label scarcity. Existing ALG methods incorporate diversity into their query strategies to maximize the gains from node sampling...
Computer Graphics Algorithms Humans Supervised Machine Learning Neural Networks, Computer
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Improved analysis of supervised learning in the RKHS with random features: Beyond least squares.

Neural networks : the official journal of the International Neural Network Society Jan 8, 2025
We consider kernel-based supervised learning using random Fourier features, focusing on its statistical error bounds and generalization properties with general loss functions. Beyond the least squares loss, existing results only demonstrate worst-cas...
Humans Least-Squares Analysis Neural Networks, Computer Supervised Machine Learning Algorithms Fourier Analysis
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Comparison of active learning algorithms in classifying head computed tomography reports using bidirectional encoder representations from transformers.

International journal of computer assisted radiology and surgery Jan 8, 2025
PURPOSE: Systems equipped with natural language (NLP) processing can reduce missed radiological findings by physicians, but the annotation costs are burden in the development. This study aimed to compare the effects of active learning (AL) algorithms...
Humans Algorithms Tomography, X-Ray Computed Problem-Based Learning Natural Language Processing Head Supervised Machine Learning Radiology Information Systems
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CLSSATP: Contrastive learning and self-supervised learning model for aquatic toxicity prediction.

Aquatic toxicology (Amsterdam, Netherlands) Jan 7, 2025
As compound concentrations in aquatic environments increase, the habitat degradation of aquatic organisms underscores the growing importance of studying the impact of chemicals on diverse aquatic populations. Understanding the potential impacts of di...
Supervised Machine Learning Water Pollutants, Chemical Aquatic Organisms Neural Networks, Computer Deep Learning
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Uncertainty Global Contrastive Learning Framework for Semi-Supervised Medical Image Segmentation.

IEEE journal of biomedical and health informatics Jan 7, 2025
In semi-supervised medical image segmentation, the issue of fuzzy boundaries for segmented objects arises. With limited labeled data and the interaction of boundaries from different segmented objects, classifying segmentation boundaries becomes chall...
Supervised Machine Learning Humans Image Interpretation, Computer-Assisted Databases, Factual Fuzzy Logic Image Processing, Computer-Assisted Diagnostic Imaging Uncertainty Algorithms
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Self-Supervised Molecular Representation Learning With Topology and Geometry.

IEEE journal of biomedical and health informatics Jan 7, 2025
Molecular representation learning is of great importance for drug molecular analysis. The development in molecular representation learning has demonstrated great promise through self-supervised pre-training strategy to overcome the scarcity of labele...
Algorithms Humans Models, Molecular Supervised Machine Learning
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