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The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases.
CLAM is a general-purpose and adaptable method that can be used for a variety of different computational pathology tasks in both clinical and research settings.
We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora.
In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e. g. the order of an event relative to a time index) can inform many important analyses.
Our experiments demonstrate that only a small number of feedback iterations are needed to train models that achieve highly competitive test set performance without access to ground truth training labels.
To estimate the transition matrix from noisy data, existing methods often need to estimate the noisy class-posterior, which could be unreliable due to the overconfidence of neural networks.
The goal is to derive conditions under which loss functions for weak-label learning are proper and lower-bounded -- two essential requirements for the losses used in class-probability estimation.
We present a framework for the induction of semantic frames from utterances in the context of an adaptive command-and-control interface.