478 research outputs found
Efficient Bayesian Uncertainty Estimation for nnU-Net
The self-configuring nnU-Net has achieved leading performance in a large
range of medical image segmentation challenges. It is widely considered as the
model of choice and a strong baseline for medical image segmentation. However,
despite its extraordinary performance, nnU-Net does not supply a measure of
uncertainty to indicate its possible failure. This can be problematic for
large-scale image segmentation applications, where data are heterogeneous and
nnU-Net may fail without notice. In this work, we introduce a novel method to
estimate nnU-Net uncertainty for medical image segmentation. We propose a
highly effective scheme for posterior sampling of weight space for Bayesian
uncertainty estimation. Different from previous baseline methods such as Monte
Carlo Dropout and mean-field Bayesian Neural Networks, our proposed method does
not require a variational architecture and keeps the original nnU-Net
architecture intact, thereby preserving its excellent performance and ease of
use. Additionally, we boost the segmentation performance over the original
nnU-Net via marginalizing multi-modal posterior models. We applied our method
on the public ACDC and M&M datasets of cardiac MRI and demonstrated improved
uncertainty estimation over a range of baseline methods. The proposed method
further strengthens nnU-Net for medical image segmentation in terms of both
segmentation accuracy and quality control
A new controller design of electro-hydraulic servo system based on empirical mode decomposition
The signal of electro-hydraulic servo system is non-stationary and time-varying due to the influence of vibration, noise and mechanical impact. The traditional digital filter always suffers delay in time domain and the delay increases along with the increasing of frequency. Considering the features of electro-hydraulic servo system, the Hilbert-Huang transform method is an effective method to decompose the original signal and obtain the noise components. Some improvements are made based on Hilbert Huang transform method and a new real time on-line filtering method is proposed in this paper. This improved filter is able to decompose out the noise components and other interference components from original signal, and remove them off in real time. Based on this new on-line filter, a new controller is also designed. Compared the filtering result with the traditional digital filter, this new controller’s control performance is much better
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