Early diagnosis and timely treatment of diabetes are critical for effective disease management and the prevention of complications. Undiagnosed diabetes can lead to an increased risk of several health issues. Although numerous machine learning (ML) m...
Prevalent studies on deep learning-based 3D medical image segmentation capture the continuous variation across 2D slices mainly via convolution, Transformer, inter-slice interaction, and time series models. In this work, via modeling this variation b...
Anomaly detection can significantly aid doctors in interpreting chest X-rays. The commonly used strategy involves utilizing the pre-trained network to extract features from normal data to establish feature representations. However, when a pre-trained...
Few-shot semantic segmentation (FSS) is of tremendous potential for data-scarce scenarios, particularly in medical segmentation tasks with merely a few labeled data. Most of the existing FSS methods typically distinguish query objects with the guidan...
Histopathological image segmentation is a laborious and time-intensive task, often requiring analysis from experienced pathologists for accurate examinations. To reduce this burden, supervised machine-learning approaches have been adopted using large...
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Feb 3, 2025
Unexpected toxicity has become a significant obstacle to drug candidate development, accounting for 30% of drug discovery failures. Traditional toxicity assessment through animal testing is costly and time-consuming. Big data and artificial intellige...
Machine-learning-based automatic sleep stage scoring is a promising approach to enhance the time-consuming manual annotation process of polysomnography recordings. Although numerous algorithms have been proposed for this purpose, systematic explorati...
International journal of medical informatics
Jan 30, 2025
BACKGROUND: Cardiac arrest (CA) is the sudden cessation of heart function, typically resulting in loss of consciousness and cessation of pulse and breathing. The electrocardiogram (ECG) stands as an essential tool extensively utilized by clinicians, ...
In this study, we present a comprehensive pipeline to train and compare a broad spectrum of machine learning and deep learning brain clocks, integrating diverse preprocessing strategies and correction terms. Our analysis also includes established met...
Medical image segmentation is pivotal in disease diagnosis and treatment. This paper presents a novel network architecture for medical image segmentation, termed TransDLNet, which is engineered to enhance the efficiency of multi-scale information uti...