18,360 research outputs found
Classification of Radiology Reports Using Neural Attention Models
The electronic health record (EHR) contains a large amount of
multi-dimensional and unstructured clinical data of significant operational and
research value. Distinguished from previous studies, our approach embraces a
double-annotated dataset and strays away from obscure "black-box" models to
comprehensive deep learning models. In this paper, we present a novel neural
attention mechanism that not only classifies clinically important findings.
Specifically, convolutional neural networks (CNN) with attention analysis are
used to classify radiology head computed tomography reports based on five
categories that radiologists would account for in assessing acute and
communicable findings in daily practice. The experiments show that our CNN
attention models outperform non-neural models, especially when trained on a
larger dataset. Our attention analysis demonstrates the intuition behind the
classifier's decision by generating a heatmap that highlights attended terms
used by the CNN model; this is valuable when potential downstream medical
decisions are to be performed by human experts or the classifier information is
to be used in cohort construction such as for epidemiological studies
Automated Detection of Usage Errors in non-native English Writing
In an investigation of the use of a novelty detection algorithm for identifying inappropriate word
combinations in a raw English corpus, we employ an
unsupervised detection algorithm based on the one-
class support vector machines (OC-SVMs) and extract
sentences containing word sequences whose frequency
of appearance is significantly low in native English
writing. Combined with n-gram language models and
document categorization techniques, the OC-SVM classifier assigns given sentences into two different
groups; the sentences containing errors and those
without errors. Accuracies are 79.30 % with bigram
model, 86.63 % with trigram model, and 34.34 % with four-gram model
Analysis of classifiers' robustness to adversarial perturbations
The goal of this paper is to analyze an intriguing phenomenon recently
discovered in deep networks, namely their instability to adversarial
perturbations (Szegedy et. al., 2014). We provide a theoretical framework for
analyzing the robustness of classifiers to adversarial perturbations, and show
fundamental upper bounds on the robustness of classifiers. Specifically, we
establish a general upper bound on the robustness of classifiers to adversarial
perturbations, and then illustrate the obtained upper bound on the families of
linear and quadratic classifiers. In both cases, our upper bound depends on a
distinguishability measure that captures the notion of difficulty of the
classification task. Our results for both classes imply that in tasks involving
small distinguishability, no classifier in the considered set will be robust to
adversarial perturbations, even if a good accuracy is achieved. Our theoretical
framework moreover suggests that the phenomenon of adversarial instability is
due to the low flexibility of classifiers, compared to the difficulty of the
classification task (captured by the distinguishability). Moreover, we show the
existence of a clear distinction between the robustness of a classifier to
random noise and its robustness to adversarial perturbations. Specifically, the
former is shown to be larger than the latter by a factor that is proportional
to \sqrt{d} (with d being the signal dimension) for linear classifiers. This
result gives a theoretical explanation for the discrepancy between the two
robustness properties in high dimensional problems, which was empirically
observed in the context of neural networks. To the best of our knowledge, our
results provide the first theoretical work that addresses the phenomenon of
adversarial instability recently observed for deep networks. Our analysis is
complemented by experimental results on controlled and real-world data
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