1 code implementation • 26 Apr 2023 • Ishaan Bhat, Josien P. W. Pluim, Max A. Viergever, Hugo J. Kuijf
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.
1 code implementation • 26 Jul 2022 • Ishaan Bhat, Josien P. W. Pluim, Hugo J. Kuijf
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.
1 code implementation • 22 Jun 2022 • Ishaan Bhat, Josien P. W. Pluim, Max A. Viergever, Hugo J. Kuijf
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.
no code implementations • 12 Jan 2021 • Ishaan Bhat, Hugo J. Kuijf, Veronika Cheplygina, Josien P. W. Pluim
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.
1 code implementation • 22 Jun 2020 • Florian List, Nicholas L. Rodd, Geraint F. Lewis, Ishaan Bhat
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).
no code implementations • 1 Oct 2019 • Florian List, Ishaan Bhat, Geraint F. Lewis
Traditionally, incorporating additional physics into existing cosmological simulations requires re-running the cosmological simulation code, which can be computationally expensive.
Cosmology and Nongalactic Astrophysics