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Found 6 papers, 4 papers with code
Effect of latent space distribution on the segmentation of images with multiple annotations
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations.
Generalized Probabilistic U-Net for medical image segementation
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations.
Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection
We study the role played by features computed from neural network uncertainty estimates and shape-based features computed from binary predictions in reducing false positives in liver lesion detection by developing a classification-based post-processing step for different uncertainty estimation methods.
Using uncertainty estimation to reduce false positives in liver lesion detection
We find that the use of a dropout rate of 0. 5 produces the least number of false positives in the neural network predictions and the trained classifier filters out approximately 90% of these false positives detections in the test-set.
The GCE in a New Light: Disentangling the $γ$-ray Sky with Bayesian Graph Convolutional Neural Networks
In simulated data, our neural network (NN) is able to reconstruct the flux of inner Galaxy emission components to on average $\sim$0. 5%, comparable to the non-Poissonian template fit (NPTF).
A black box for dark sector physics: Predicting dark matter annihilation feedback with conditional GANs
Traditionally, incorporating additional physics into existing cosmological simulations requires re-running the cosmological simulation code, which can be computationally expensive.
Cosmology and Nongalactic Astrophysics
