AI Medical Compendium Topic

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Unsupervised Machine Learning

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Identifying diseases symptoms and general rules using supervised and unsupervised machine learning.

Scientific reports
The symptoms of diseases can vary among individuals and may remain undetected in the early stages. Detecting these symptoms is crucial in the initial stage to effectively manage and treat cases of varying severity. Machine learning has made major adv...

MSRA-Net: multi-channel semantic-aware and residual attention mechanism network for unsupervised 3D image registration.

Physics in medicine and biology
. Convolutional neural network (CNN) is developing rapidly in the field of medical image registration, and the proposed U-Net further improves the precision of registration. However, this method may discard certain important information in the proces...

AutoCorNN: An Unsupervised Physics-Aware Deep Learning Model for Geometric Distortion Correction of Brain MRI Images Towards MR-Only Stereotactic Radiosurgery.

Journal of imaging informatics in medicine
Geometric distortions in brain MRI images arising from susceptibility artifacts at air-tissue interfaces pose a significant challenge for high-precision radiation therapy modalities like stereotactic radiosurgery, necessitating sub-millimeter accurac...

Unsupervised domain adaptive building semantic segmentation network by edge-enhanced contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Unsupervised domain adaptation (UDA) is a weakly supervised learning technique that classifies images in the target domain when the source domain has labeled samples, and the target domain has unlabeled samples. Due to the complexity of imaging condi...

Communicating exploratory unsupervised machine learning analysis in age clustering for paediatric disease.

BMJ health & care informatics
BACKGROUND: Despite the increasing availability of electronic healthcare record (EHR) data and wide availability of plug-and-play machine learning (ML) Application Programming Interfaces, the adoption of data-driven decision-making within routine hos...

An unsupervised multi-view contrastive learning framework with attention-based reranking strategy for entity alignment.

Neural networks : the official journal of the International Neural Network Society
Entity alignment is a crucial task in knowledge graphs, aiming to match corresponding entities from different knowledge graphs. Due to the scarcity of pre-aligned entities in real-world scenarios, research focused on unsupervised entity alignment has...

Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning.

Biophysical journal
Protein-DNA interactions and protein-mediated DNA compaction play key roles in a range of biological processes. The length scales typically involved in DNA bending, bridging, looping, and compaction (≥1 kbp) are challenging to address experimentally ...

DECNet: Dense embedding contrast for unsupervised semantic segmentation.

Neural networks : the official journal of the International Neural Network Society
Unsupervised semantic segmentation is important for understanding that each pixel belongs to known categories without annotation. Recent studies have demonstrated promising outcomes by employing a vision transformer backbone pre-trained on an image-l...

Deep coherence learning: An unsupervised deep beamformer for high quality single plane wave imaging in medical ultrasound.

Ultrasonics
Plane wave imaging (PWI) in medical ultrasound is becoming an important reconstruction method with high frame rates and new clinical applications. Recently, single PWI based on deep learning (DL) has been studied to overcome lowered frame rates of tr...