Semi-supervised learning (SSL) methods show their powerful performance to deal with the issue of data shortage in the field of medical image segmentation. However, existing SSL methods still suffer from the problem of unreliable predictions on unanno...
Popular semi-supervised medical image segmentation networks often suffer from error supervision from unlabeled data since they usually use consistency learning under different data perturbations to regularize model training. These networks ignore the...
Traditional Chinese medicine (TCM) has gradually played an indispensable role in people's health maintenance, especially in the treatment of chronic diseases. However, there is always uncertainty and hesitation in the judgment and understanding of di...
Neural networks : the official journal of the International Neural Network Society
Apr 28, 2023
The stabilization problem is studied for memristive neural networks with interval delays under aperiodic switching event-triggered control. Note that, most of delayed memristive neural networks models studied are discontinuous, which are not the real...
Many popular survival models rely on restrictive parametric, or semiparametric, assumptions that could provide erroneous predictions when the effects of covariates are complex. Modern advances in computational hardware have led to an increasing inter...
Statistical methods in medical research
Apr 3, 2023
The uncertainty in predictions from deep neural network analysis of medical imaging is challenging to assess but potentially important to include in subsequent decision-making. Using data from diabetic retinopathy detection, we present an empirical e...
This study presents a novel approach to cope with the human behaviour uncertainty during Human-Robot Collaboration (HRC) in dynamic and unstructured environments, such as agriculture, forestry, and construction. These challenging tasks, which often r...
BACKGROUND: Image-guided neurosurgery requires high localization and registration accuracy to enable effective treatment and avoid complications. However, accurate neuronavigation based on preoperative magnetic resonance (MR) or computed tomography (...
BACKGROUND AND OBJECTIVE: Diagnostic uncertainty, when unrecognized or poorly communicated, can result in diagnostic error. However, diagnostic uncertainty is challenging to study due to a lack of validated identification methods. This study aims to ...
Machine learning (ML) algorithms to detect critical findings on head CTs may expedite patient management. Most ML algorithms for diagnostic imaging analysis utilize dichotomous classifications to determine whether a specific abnormality is present. H...
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