AIMC Topic: Machine Learning

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[Research progress on quantitative magnetic susceptibility imaging reconstruction method based on improved U-network model].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Quantitative magnetic susceptibility imaging (QSM) is an imaging method based on magnetic resonance imaging (MRI) phase signal processing and inversion to obtain tissue magnetic susceptibility distribution, which can generate images reflecting the ma...

[Detection of neurofibroma combining radiomics and ensemble learning].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
This study proposes an automated neurofibroma detection method for whole-body magnetic resonance imaging (WBMRI) based on radiomics and ensemble learning. A dynamic weighted box fusion mechanism integrating two dimensional (2D) object detection and t...

[Feature distillation multiple instance learning method based on sequence reorganized Mamba].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Prostate cancer is one of the most prevalent malignancies among men worldwide, and its diagnosis relies heavily on accurate analysis of whole slide imaging (WSI) in histopathology. However, manual interpretation is time-consuming and prone to inconsi...

Using Machine Learning Algorithms to Identify Key Predictors of Invasive Mold Infection Surveillance.

The Journal of infectious diseases
BACKGROUND: Invasive mold infections (IMI) can lead to severe morbidity and mortality, but routine public health surveillance is lacking. Although extensive evaluation is needed for clinical diagnosis, case classification prediction models may inform...

[Identification of high-risk preoperative blood indicators and baseline characteristics for multiple postoperative complications in rheumatoid arthritis patients undergoing total knee arthroplasty: a multi-machine learning feature contribution analysis].

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery
OBJECTIVE: To explore, identify, and develop novel blood-based indicators using machine learning algorithms for accurate preoperative assessment and effective prediction of postoperative complication risks in patients with rheumatoid arthritis (RA) u...

An Interpretable Machine Learning Model Based on Metabolomics for Predicting Plaque Burden in Cryptogenic Stroke.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Cryptogenic stroke represents 25%-40% of ischemic strokes, with many cases harboring unrecognized large artery atherosclerosis (LAA) requiring specific secondary prevention. In this multicenter pilot study, we developed a metabolomics-based machine l...

A Roadmap for the Future of Systems Biology in Cancer Research.

Cancer research
Cancer systems biology seeks to understand how cancer arises as a system of interconnected molecules, cells, and tissues, with the goal of understanding, predicting, and controlling the disease. In the last decade, the field has rapidly grown as adva...

Evaluation of Bio-Inspired Models under Different Learning Settings for Energy Efficiency in Network Traffic Prediction.

International journal of neural systems
Cellular traffic forecasting is a critical task that enables network operators to efficiently allocate resources and address anomalies in rapidly evolving environments. The exponential growth of data collected from base stations poses significant cha...

Machine learning-driven prediction of readmission risk in heart failure patients with diabetes: synergistic assessment of inflammatory and metabolic biomarkers.

International journal of cardiology
BACKGROUND: Heart failure (HF) and diabetes mellitus (DM) frequently coexist, exacerbating disease progression and increasing hospital readmission risk. Accurate prediction of readmission in HF patients with DM remains a clinical challenge. This stud...