AIMC Topic: Unsupervised Machine Learning

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Reducing bias in source-free unsupervised domain adaptation for regression.

Neural networks : the official journal of the International Neural Network Society Jan 17, 2025
Due to data privacy and storage concerns, Source-Free Unsupervised Domain Adaptation (SFUDA) focuses on improving an unlabelled target domain by leveraging a pre-trained source model without access to source data. While existing studies attempt to tr...
Bias Regression Analysis Unsupervised Machine Learning Humans Neural Networks, Computer
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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...
Algorithms Risk Assessment Humans Support Vector Machine Cluster Analysis Unsupervised Machine Learning Supervised Machine Learning
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Dual-view global and local category-attentive domain alignment for unsupervised conditional adversarial domain adaptation.

Neural networks : the official journal of the International Neural Network Society Jan 8, 2025
Conditional adversarial domain adaptation (CADA) is one of the most commonly used unsupervised domain adaptation (UDA) methods. CADA introduces multimodal information to the adversarial learning process to align the distributions of the labeled sourc...
Unsupervised Machine Learning Neural Networks, Computer Algorithms Humans
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Unsupervised deep learning-based medical image registration: a survey.

Physics in medicine and biology Jan 7, 2025
In recent decades, medical image registration technology has undergone significant development, becoming one of the core technologies in medical image analysis. With the rise of deep learning, deep learning-based medical image registration methods ha...
Unsupervised Machine Learning Diagnostic Imaging Deep Learning Image Processing, Computer-Assisted Humans
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Contrastive Registration for Unsupervised Medical Image Segmentation.

IEEE transactions on neural networks and learning systems Jan 7, 2025
Medical image segmentation is an important task in medical imaging, as it serves as the first step for clinical diagnosis and treatment planning. While major success has been reported using deep learning supervised techniques, they assume a large and...
Neural Networks, Computer Algorithms Humans Image Processing, Computer-Assisted Unsupervised Machine Learning Diagnostic Imaging Databases, Factual Deep Learning
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Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease states.

Scientific reports Jan 4, 2025
Prostate cancer is a disease which poses an interesting clinical question: Should it be treated? Only a small subset of prostate cancers are aggressive and require removal and treatment to prevent metastatic spread. However, conventional diagnostics ...
Unsupervised Machine Learning Prostatic Neoplasms Prostate Cell Line, Tumor Spectrum Analysis, Raman Humans Male
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Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

Pharmacotherapy Jan 3, 2025
BACKGROUND: Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be preventable. Intravenous medications (including administered volume) are a primary cause for FO but are challenging to evaluate as a FO predictor given th...
Water-Electrolyte Imbalance Humans Aged Intensive Care Units Male Female Middle Aged Machine Learning Cohort Studies Retrospective Studies Unsupervised Machine Learning
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Tumor detection on bronchoscopic images by unsupervised learning.

Scientific reports Jan 2, 2025
The diagnosis and early identification of intratracheal tumors relies on the experience of the operators and the specialists. Operations by physicians with insufficient experience may lead to misdiagnosis or misjudgment of tumors. To address this iss...
Lung Neoplasms Bronchoscopy Unsupervised Machine Learning Algorithms Image Processing, Computer-Assisted Humans
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Unsupervised Non-Rigid Histological Image Registration Guided by Keypoint Correspondences Based on Learnable Deep Features With Iterative Training.

IEEE transactions on medical imaging Jan 2, 2025
Histological image registration is a fundamental task in histological image analysis. It is challenging because of substantial appearance differences due to multiple staining. Keypoint correspondences, i.e., matched keypoint pairs, have been introduc...
Databases, Factual Deep Learning Image Processing, Computer-Assisted Humans Algorithms Unsupervised Machine Learning
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OTMorph: Unsupervised Multi-Domain Abdominal Medical Image Registration Using Neural Optimal Transport.

IEEE transactions on medical imaging Jan 2, 2025
Deformable image registration is one of the essential processes in analyzing medical images. In particular, when diagnosing abdominal diseases such as hepatic cancer and lymphoma, multi-domain images scanned from different modalities or different ima...
Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Image Processing, Computer-Assisted Neural Networks, Computer Abdomen Humans Algorithms Unsupervised Machine Learning
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