1,429 research outputs found
Corpus Augmentation by Sentence Segmentation for Low-Resource Neural Machine Translation
Neural Machine Translation (NMT) has been proven to achieve impressive
results. The NMT system translation results depend strongly on the size and
quality of parallel corpora. Nevertheless, for many language pairs, no
rich-resource parallel corpora exist. As described in this paper, we propose a
corpus augmentation method by segmenting long sentences in a corpus using
back-translation and generating pseudo-parallel sentence pairs. The experiment
results of the Japanese-Chinese and Chinese-Japanese translation with
Japanese-Chinese scientific paper excerpt corpus (ASPEC-JC) show that the
method improves translation performance.Comment: 4 pages. The version before Applied. Science
Direct estimation of kinetic parametric images for dynamic PET.
Dynamic positron emission tomography (PET) can monitor spatiotemporal distribution of radiotracer in vivo. The spatiotemporal information can be used to estimate parametric images of radiotracer kinetics that are of physiological and biochemical interests. Direct estimation of parametric images from raw projection data allows accurate noise modeling and has been shown to offer better image quality than conventional indirect methods, which reconstruct a sequence of PET images first and then perform tracer kinetic modeling pixel-by-pixel. Direct reconstruction of parametric images has gained increasing interests with the advances in computing hardware. Many direct reconstruction algorithms have been developed for different kinetic models. In this paper we review the recent progress in the development of direct reconstruction algorithms for parametric image estimation. Algorithms for linear and nonlinear kinetic models are described and their properties are discussed
Comparison of Lesion Detection and Quantification in MAP Reconstruction with Gaussian and Non-Gaussian Priors
Statistical image reconstruction methods based on
maximum a posteriori (MAP) principle have been developed for
emission tomography. The prior distribution of the unknown image
plays an important role in MAP reconstruction. The most commonly
used prior are Gaussian priors, whose logarithm has a quadratic
form. Gaussian priors are relatively easy to analyze. It has been
shown that the effect of a Gaussian prior can be approximated by
linear filtering a maximum likelihood (ML) reconstruction. As a
result, sharp edges in reconstructed images are not preserved. To
preserve sharp transitions, non-Gaussian priors have been
proposed. However, their effect on clinical tasks is less obvious.
In this paper, we compare MAP reconstruction with Gaussian and
non-Gaussian priors for lesion detection and region of interest
quantification using computer simulation. We evaluate three
representative priors: Gaussian prior, Huber prior, and
Geman-McClure prior. We simulate imaging a prostate tumor using
positron emission tomography (PET). The detectability of a known
tumor in either a fixed background or a random background is
measured using a channelized Hotelling observer. The bias-variance
tradeoff curves are calculated for quantification of the total
tumor activity. The results show that for the detection and
quantification tasks, the Gaussian prior is as effective as
non-Gaussian priors
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