AIMC Topic: Unsupervised Machine Learning

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Unsupervised cross-modality domain adaptation via source-domain labels guided contrastive learning for medical image segmentation.

Medical & biological engineering & computing
Unsupervised domain adaptation (UDA) offers a promising approach to enhance discriminant performance on target domains by utilizing domain adaptation techniques. These techniques enable models to leverage knowledge from the source domain to adjust to...

An efficient approach on risk factor prediction related to cardiovascular disease around Kumbakonam, Tamil Nadu, India, using unsupervised machine learning techniques.

Scientific reports
Nowadays, human beings suffer from varieties of diseases due to the environmental circumstances and their residing habits. Cardiovascular diseases (CVD) are the leading cause of mortality among all diseases. CVDs are heart-related diseases. In early ...

PrediRep: Modeling hierarchical predictive coding with an unsupervised deep learning network.

Neural networks : the official journal of the International Neural Network Society
Hierarchical predictive coding (hPC) provides a compelling framework for understanding how the cortex predicts future sensory inputs by minimizing prediction errors through an internal generative model of the external world. Existing deep learning mo...

Unsupervised machine learning clustering approach for hospitalized COVID-19 pneumonia patients.

BMC pulmonary medicine
BACKGROUND: Identification of distinct clinical phenotypes of diseases can guide personalized treatment. This study aimed to classify hospitalized COVID-19 pneumonia subgroups using an unsupervised machine learning approach.

An investigation of microbial groundwater contamination seasonality and extreme weather event interruptions using "big data", time-series analyses, and unsupervised machine learning.

Environmental pollution (Barking, Essex : 1987)
Temporal studies of groundwater potability have historically focused on E. coli detection rates, with non-E. coli coliforms (NEC) and microbial concentrations remaining understudied by comparison. Additionally, "big data" (i.e., large, diverse datase...

CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition.

IEEE transactions on neural networks and learning systems
This article proposes a semi-supervised contrastive capsule transformer method with feature-based knowledge distillation (KD) that simplifies the existing semisupervised learning (SSL) techniques for wearable human activity recognition (HAR), called ...

UTSRMorph: A Unified Transformer and Superresolution Network for Unsupervised Medical Image Registration.

IEEE transactions on medical imaging
Complicated image registration is a key issue in medical image analysis, and deep learning-based methods have achieved better results than traditional methods. The methods include ConvNet-based and Transformer-based methods. Although ConvNets can eff...

Facing Differences of Similarity: Intra- and Inter-Correlation Unsupervised Learning for Chest X-Ray Anomaly Detection.

IEEE transactions on medical imaging
Anomaly detection can significantly aid doctors in interpreting chest X-rays. The commonly used strategy involves utilizing the pre-trained network to extract features from normal data to establish feature representations. However, when a pre-trained...

Harmonizing flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization.

Medical image analysis
Lack of standardization and various intrinsic parameters for magnetic resonance (MR) image acquisition results in heterogeneous images across different sites and devices, which adversely affects the generalization of deep neural networks. To alleviat...

Identifying Novel Emotions and Wellbeing of Horses from Videos Through Unsupervised Learning.

Sensors (Basel, Switzerland)
This research applies unsupervised learning on a large original dataset of horses in the wild to identify previously unidentified horse emotions. We construct a novel, high-quality, diverse dataset of 3929 images consisting of five wild horse breeds ...