AIMC Topic: Female

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Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.

Medical image analysis
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor prolifera...

Decision Fusion-Based Fetal Ultrasound Image Plane Classification Using Convolutional Neural Networks.

Ultrasound in medicine & biology
Machine learning for ultrasound image analysis and interpretation can be helpful in automated image classification in large-scale retrospective analyses to objectively derive new indicators of abnormal fetal development that are embedded in ultrasoun...

Movement time and guidance accuracy in teleoperation of robotic vehicles.

Ergonomics
Two experiments are reported on the steering of a tracked vehicle through straight-line courses and corners to determine the relationships between movement time and control accuracy with the geometry of the course, such as the vehicle width, the trac...

Stress detection in daily life scenarios using smart phones and wearable sensors: A survey.

Journal of biomedical informatics
Stress has become a significant cause for many diseases in the modern society. Recently, smartphones, smartwatches and smart wrist-bands have become an integral part of our lives and have reached a widespread usage. This raised the question of whethe...

Machine learning-based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics.

ESC heart failure
AIMS: Machine learning (ML) is widely believed to be able to learn complex hidden interactions from the data and has the potential in predicting events such as heart failure (HF) readmission and death. Recent studies have revealed conflicting results...

Functional brain networks and neuroanatomy underpinning nausea severity can predict nausea susceptibility using machine learning.

The Journal of physiology
KEY POINTS: Nausea is an adverse experience characterised by alterations in autonomic and cerebral function. Susceptibility to nausea is difficult to predict, but machine learning has yet to be applied to this field of study. The severity of nausea t...

Non-invasive machine learning estimation of effort differentiates sleep-disordered breathing pathology.

Physiological measurement
OBJECTIVE: Obstructive sleep-disordered breathing (SDB) events, unlike central events, are associated with increased respiratory effort. Esophageal pressure (P ) monitoring is the gold standard for measuring respiratory effort, but it is typically po...

Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification.

Human brain mapping
Early and accurate mild cognitive impairment (MCI) detection within a heterogeneous, nonclinical population is needed to improve care for persons at risk of developing dementia. Magnetic resonance imaging (MRI)-based classification may aid early diag...

Machine learning models based on the dimensionality reduction of standard automated perimetry data for glaucoma diagnosis.

Artificial intelligence in medicine
INTRODUCTION: Visual field testing via standard automated perimetry (SAP) is a commonly used glaucoma diagnosis method. Applying machine learning techniques to the visual field test results, a valid clinical diagnosis of glaucoma solely based on the ...