AIMC Topic: Machine Learning

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Quality Control in Digital Pathology: Automatic Fragment Detection and Counting.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Manual assessment of fragments during the pro-cessing of pathology specimens is critical to ensure that the material available for slide analysis matches that captured during grossing without losing valuable material during this process. However, thi...

Using Natural Language Processing of Clinical Notes to Predict Outcomes of Opioid Treatment Program.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Potential of natural language processing (NLP) in extracting patient's information from clinical notes of opioid treatment programs (OTP) and leveraging it in development of predictive models has not been fully explored. The goal of this study was to...

A Comparative Study of Deep Learning Algorithms for Detecting Food Intake.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The choice of appropriate machine learning algorithms is crucial for classification problems. This study compares the performance of state-of-the-art time-series deep learning algorithms for classifying food intake using sensor signals. The sensor si...

Label Alignment Improves EEG-based Machine Learning-based Classification of Traumatic Brain Injury.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Machine learning and deep learning algorithms have paved the way for improved analysis of biomedical data which has led to a better understanding of various biological conditions. However, one major hindrance to leveraging the potential of machine le...

ATLAS: An Adaptive Transfer Learning Based Pain Assessment System: A Real Life Unsupervised Pain Assessment Solution.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Undertreatment or overtreatment of pain will cause severe consequences physiologically and psychologically. Thus, researchers have made great efforts to develop automatic pain assessment approaches based on physiological signals using machine learnin...

PySio: A New Python Toolbox for Physiological Signal Visualization and Feature Analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In physiological signal analysis, identifying meaningful relationships and inherent patterns in signals can provide valuable information regarding subjects' physiological state and changes. Although MATLAB has been widely used in signal processing an...

Stress Inference from Abdominal Sounds using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Stress is often considered the 21 century's epidemic, affecting more than a third of the globe's population. Long-term exposure to stress has significant side effects on physical and mental health. In this work we propose a methodology for detecting ...

Reducing echo state network size with controllability matrices.

Chaos (Woodbury, N.Y.)
Echo state networks are a fast training variant of recurrent neural networks excelling at approximating nonlinear dynamical systems and time series prediction. These machine learning models act as nonlinear fading memory filters. While these models b...

Dark soliton detection using persistent homology.

Chaos (Woodbury, N.Y.)
Classifying images often requires manual identification of qualitative features. Machine learning approaches including convolutional neural networks can achieve accuracy comparable to human classifiers but require extensive data and computational res...