AIMC Topic: Unsupervised Machine Learning

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Unsupervised Bidirectional Contrastive Reconstruction and Adaptive Fine-Grained Channel Attention Networks for image dehazing.

Neural networks : the official journal of the International Neural Network Society
Recently, Unsupervised algorithms has achieved remarkable performance in image dehazing. However, the CycleGAN framework can lead to confusion in generator learning due to inconsistent data distributions, and the DisentGAN framework lacks effective c...

Unsupervised model adaptation for source-free segmentation of medical images.

Medical image analysis
The recent prevalence of deep neural networks has led semantic segmentation networks to achieve human-level performance in the medical field, provided they are given sufficient training data. However, these networks often fail to generalize when task...

Exploring tumor heterogeneity in colorectal liver metastases by imaging: Unsupervised machine learning of preoperative CT radiomics features for prognostic stratification.

European journal of radiology
OBJECTIVES: This study aimed to investigate tumor heterogeneity of colorectal liver metastases (CRLM) and stratify the patients into different risk groups of prognoses following liver resection by applying an unsupervised radiomics machine-learning a...

A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation.

Medical image analysis
Quantitative susceptibility mapping (QSM) is an MRI-based technique that estimates the underlying tissue magnetic susceptibility based on phase signal. Deep learning (DL)-based methods have shown promise in handling the challenging ill-posed inverse ...

Self-paced regularized adaptive multi-view unsupervised feature selection.

Neural networks : the official journal of the International Neural Network Society
Multi-view unsupervised feature selection (MUFS) is an efficient approach for dimensional reduction of heterogeneous data. However, existing MUFS approaches mostly assign the samples the same weight, thus the diversity of samples is not utilized effi...

Symptom-based drug prediction of lifestyle-related chronic diseases using unsupervised machine learning techniques.

Computers in biology and medicine
BACKGROUND AND OBJECTIVES: Lifestyle-related diseases (LSDs) impose a substantial economic burden on patients and health care services. LSDs are chronic in nature and can directly affect the heart and lungs. Therapeutic interventions only based on sy...

Using unsupervised learning to classify inlet water for more stable design of water reuse in industrial parks.

Water science and technology : a journal of the International Association on Water Pollution Research
The water reuse facilities of industrial parks face the challenge of managing a growing variety of wastewater sources as their inlet water. Typically, this clustering outcome is designed by engineers with extensive expertise. This paper presents an i...

An Unsupervised Machine Learning Approach for the Automatic Construction of Local Chemical Descriptors.

Journal of chemical information and modeling
Condensing the many physical variables defining a chemical system into a fixed-size array poses a significant challenge in the development of chemical Machine Learning (ML). Atom Centered Symmetry Functions (ACSFs) offer an intuitive featurization ap...

Source-free unsupervised domain adaptation: A survey.

Neural networks : the official journal of the International Neural Network Society
Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different domains. Existing UDA approaches highly depend on the accessibility of sou...

Cluster-Based Toxicity Estimation of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification.

International journal of radiation oncology, biology, physics
PURPOSE: Given the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of d...