We introduce CASED, a novel curriculum sampling algorithm that facilitates the optimization of deep learning segmentation or detection models on data sets with extreme class imbalance. We evaluate the CASED learning framework on the task of lung nodule detection in chest CT. In contrast to two-stage solutions, wherein nodule candidates are first proposed by a segmentation model and refined by a second detection stage, CASED improves the training of deep nodule segmentation models (e.g. UNet) to the point where state of the art results are achieved using only a trivial detection stage. CASED improves the optimization of deep segmentation models by allowing them to first learn how to distinguish nodules from their immediate surroundings, while continuously adding a greater proportion of difficult-to-classify global context, until uniformly sampling from the empirical data distribution. Using CASED during training yields a minimalist proposal to the lung nodule detection problem that tops the LUNA16 nodule detection benchmark with an average sensitivity score of 88.35%. Furthermore, we find that models trained using CASED are robust to nodule annotation quality by showing that comparable results can be achieved when only a point and radius for each ground truth nodule are provided during training. Finally, the CASED learning framework makes no assumptions with regard to imaging modality or segmentation target and should generalize to other medical imaging problems where class imbalance is a persistent problem.
In this paper the use of acoustic similarity of speech intervals for generating improved confidence scores for spoken term detection (STD) is investigated. A procedure based on acoustic dotplots which requires no training data is deployed for discovering similar speech intervals. A graph based random walk algorithm incorporates acoustic similarity of hypothesized term occurrences for improving the corresponding confidence scores. The proposed approach is evaluated in an open vocabulary STD task defined on a lecture domain corpus. It is shown that updating the confidence scores in this fashion results in a significant increase in term detection performance of out of vocabulary search terms. A relative improvement of 12.9% in figure of merit was gained relative to that obtained from a baseline lattice based STD system.
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