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Found 5 papers, 4 papers with code
Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation
The work discusses the use of machine learning algorithms for anomaly detection in medical image analysis and how the performance of these algorithms depends on the number of annotators and the quality of labels.
Learning for Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification
Transfer learning plays an essential role in Deep Learning, which can remarkably improve the performance of the target domain, whose training data is not sufficient.
Enhancing Deep Learning-based 3-lead ECG Classification with Heartbeat Counting and Demographic Data Integration
Nowadays, an increasing number of people are being diagnosed with cardiovascular diseases (CVDs), the leading cause of death globally.
LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification
In clinical practices and most of the current research, standard 12-lead ECG is mainly used.
Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis
As a result, the labeled data may contain a variety of human biases with a high rate of disagreement among annotators, which significantly affect the performance of supervised machine learning algorithms.
