Magnetic particle tracking is a recently developed technology that can measure the translation and rotation of a particle in an opaque environment like a turbidity flow and fluidized-bed flow. The trajectory reconstruction usually relies on numerical...
IEEE transactions on neural networks and learning systems
Jul 6, 2021
We consider a human-in-the-loop scenario in the context of low-shot learning. Our approach was inspired by the fact that the viability of samples in novel categories cannot be sufficiently reflected by those limited observations. Some heterogeneous s...
Neural networks : the official journal of the International Neural Network Society
Jun 29, 2021
This paper investigates the robust synchronization problem for a class of master-slave neural networks (MSNNs) subject to network-induced delays, unknown time-varying uncertainty, and exogenous disturbances. An equivalent-input-disturbance (EID) esti...
Deep co-training has recently been proposed as an effective approach for image segmentation when annotated data is scarce. In this paper, we improve existing approaches for semi-supervised segmentation with a self-paced and self-consistent co-trainin...
Segmentation of ovary and follicles from 3D ultrasound (US) is the crucial technique of measurement tools for female infertility diagnosis. Since manual segmentation is time-consuming and operator-dependent, an accurate and fast segmentation method i...
Risk analysis : an official publication of the Society for Risk Analysis
Jun 19, 2021
In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificia...
: Computational modeling has rapidly advanced over the last decades. Recently, machine learning has emerged as a powerful and cost-effective strategy to learn from existing datasets and perform predictions on unseen molecules. Accordingly, the explos...
Neural networks : the official journal of the International Neural Network Society
Jun 12, 2021
Most deep neural networks (DNNs) are trained with large amounts of noisy labels when they are applied. As DNNs have the high capacity to fit any noisy labels, it is known to be difficult to train DNNs robustly with noisy labels. These noisy labels ca...
Medical decision making : an international journal of the Society for Medical Decision Making
Jun 8, 2021
BACKGROUND: Metamodeling may substantially reduce the computational expense of individual-level state transition simulation models (IL-STM) for calibration, uncertainty quantification, and health policy evaluation. However, because of the lack of gui...
Journal of chemical information and modeling
May 27, 2021
Predictive modeling for toxicity can help reduce risks in a range of applications and potentially serve as the basis for regulatory decisions. However, the utility of these predictions can be limited if the associated uncertainty is not adequately qu...