Machine learning model improves accuracy of liquid biopsy results

A machine learning model developed by researchers at the Johns Hopkins Kimmel Cancer Center filters out the biological noise ...
News-Medical.Net

Revolutionizing calibration: How Lumo uses machine learning to streamline E&L screening and toxicological risk assessment

Lumo leverages advanced machine learning to reduce calibration time, and flag low-confidence response factor predictions.
News-Medical.Net

Machine learning model filters out the biological noise in liquid biopsy samples

A machine learning model developed by researchers at the Johns Hopkins Kimmel Cancer Center filters out the biological noise in liquid biopsy samples, helping clinicians better match therapies to ...
IEEE

Detection of Different Stages of Anxiety From Single-Channel Wearable ECG Sensor Signal Using Fourier–Bessel Domain Adaptive Wavelet Transform

Abstract: In this letter, the Fourier–Bessel domain adaptive wavelet transform (FBDAWT) is proposed for the automated detection of anxiety stages using the single-channel wearable electrocardiogram ...
The Lancet

Deep learning for H&E-based meningioma molecular classification and outcome prediction: a retrospective cohort study

To our knowledge, this study is the first to apply deep learning models that can, beyond diagnosis, identify molecular subtypes and predict outcomes in a single brain tumour entity (meningioma) using ...
IEEE

Assessing the Reidentification Risks Posed by Deep Learning Algorithms Applied to ECG Data

Abstract: ECG (Electrocardiogram) data analysis is one of the most widely used and important tools in cardiology diagnostics. In recent years the development of advanced deep learning techniques and ...