The framework employs a novel self-paced sampling strategy to ensure the accuracy of pseudo labels.
The resulting representations and clusters from self-supervision are used as features of a survival model for recurrence prediction at the patient level.
no code implementations • 21 Nov 2021 • Sheng Liu, Aakash Kaku, Weicheng Zhu, Matan Leibovich, Sreyas Mohan, Boyang Yu, Haoxiang Huang, Laure Zanna, Narges Razavian, Jonathan Niles-Weed, Carlos Fernandez-Granda
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty.
We discover a phenomenon that has been previously reported in the context of classification: the networks tend to first fit the clean pixel-level labels during an "early-learning" phase, before eventually memorizing the false annotations.
A feasible approach to improving the representation learning of EHR data is to associate relevant medical concepts and utilize these connections.
In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform.
In addition, we propose a transformation ranking algorithm that is very stable to large variances in network prior probabilities, a common issue that arises in medical applications of Bayesian networks.