Computer methods and programs in biomedicine
Nov 4, 2023
BACKGROUND AND OBJECTIVES: Detection and classification of heart murmur using mobile-phone-collected sound is an emerging approach to the scale-up screening of valvular heart disease at a population level. Nonetheless, the widespread adoption of arti...
The survival analysis on histological whole-slide images (WSIs) is one of the most important means to estimate patient prognosis. Although many weakly-supervised deep learning models have been developed for gigapixel WSIs, their potential is generall...
Medical & biological engineering & computing
Oct 25, 2023
Semi-supervised learning methods have been attracting much attention in medical image segmentation due to the lack of high-quality annotation. To cope with the noise problem of pseudo-label in semi-supervised medical image segmentation and the limita...
The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, t...
IEEE journal of biomedical and health informatics
Oct 5, 2023
Distributed big data and digital healthcare technologies have great potential to promote medical services, but challenges arise when it comes to learning predictive model from diverse and complex e-health datasets. Federated Learning (FL), as a colla...
IEEE journal of biomedical and health informatics
Oct 5, 2023
New technologies are transforming medicine, and this revolution starts with data. Usually, health services within public healthcare systems are accessed through a booking centre managed by local health authorities and controlled by the regional gover...
. Although convolutional neural networks (CNN) and Transformers have performed well in many medical image segmentation tasks, they rely on large amounts of labeled data for training. The annotation of medical image data is expensive and time-consumin...
A common problem with segmentation of medical images using neural networks is the difficulty to obtain a significant number of pixel-level annotated data for training. To address this issue, we proposed a semi-supervised segmentation network based on...
Neural networks : the official journal of the International Neural Network Society
Sep 29, 2023
A self-training algorithm is a classical semi-supervised learning algorithm that uses a small number of labeled samples and a large number of unlabeled samples to train a classifier. However, the existing self-training algorithms consider only the ge...
BACKGROUND: Drug-target binding affinity (DTA) prediction is important for the rapid development of drug discovery. Compared to traditional methods, deep learning methods provide a new way for DTA prediction to achieve good performance without much k...