Titlebook: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging; 6th International Wo Carole H. Sudre,Raghav Mehta,William M. Wells

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Harry Anthony,Konstantinos Kamnitsasch small cosmic scales. Physicists take the flatness of space for granted in regions of that size. But it is good to finally have a mathematical confirmation in this sense.Our main goals, however, are mathematical. We will shed some light on the dynamics of N point masses that move in spaces of non-

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Biraja Ghoshal,William Woof,Bernardo Mendes,Saoud Al-Khuzaei,Thales Antonio Cabral De Guimaraes,Male

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Ufaq Khan,Umair Nawaz,Tooba T. Sheikh,Asif Hanif,Mohammad YaqubPeer to Peer

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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging6th International Wo

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Uncertainty-Aware Bayesian Deep Learning with Noisy Training Labels for Epileptic Seizure Detection existing detection model and trained using a novel KL divergence-based loss function. We validate BUNDL on both a simulated EEG dataset and the Temple University Hospital (TUH) corpus using three state-of-the-art deep networks. In all cases, BUNDL improves seizure detection performance over existin

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Active Learning for Scribble-Based Diffusion MRI Segmentation effectively suppresses false positives that arise when generalizing from sparse scribbles. Taken together, these contributions substantially improve the accuracy that can be achieved with various annotation budgets.