Organ segmentation is a fundamental task in medical imaging, and it is useful
for many clinical automation pipelines. Typically, the process involves
segmenting the entire volume, which can be unnecessary when the points of
interest are limited. In those cases, a classifier could be used instead of
segmentation. However, there is an inherent trade-off between the context size
and the speed of classifiers. To address this issue, we propose a new method
that employs a data selection strategy with sparse sampling across a wide field
of view without image resampling. This sparse sampling strategy makes it
possible to classify voxels into multiple organs in real time without using
accelerators. Although our method is an independent classifier, it can generate
full segmentation by querying grid locations at any resolution. We have
compared our method with existing segmentation techniques, demonstrating its
potential for superior runtime in practical applications in medical imaging.

在医学影像中，我们提出了一种新方法，通过稀疏采样策略跨广泛视野进行数据选择，实时对多个器官进行分类，从而在不使用加速器的情况下生成全面分割，与现有的分割技术相比，展示了超 ior 优越的运行时潜力。

计算机断层扫描图像上的实时多器官分类

Real Time Multi Organ Classification on Computed Tomography Images

Noisy Student Training (NST) has recently demonstrated extremely strong
performance in Automatic Speech Recognition(ASR). In this paper, we propose a
data selection strategy named LM Filter to improve the performance of NST on
non-target domain data in ASR tasks. Hypotheses with and without a Language
Model are generated and the CER differences between them are utilized as a
filter threshold. Results reveal that significant improvements of 10.4%
compared with no data filtering baselines. We can achieve 3.31% CER in
AISHELL-1 test set, which is best result from our knowledge without any other
supervised data. We also perform evaluations on the supervised 1000 hour
AISHELL-2 dataset and competitive results of 4.73% CER can be achieved.

本篇研究提出了一种数据选择策略（称为 LM Filter），目的是提高 Noisy Student Training 在自动语音识别中对非目标领域数据的表现。使用带和不带语言模型的假设，其 CER 差异用作过滤阈值，结果显示相比较没有数据过滤的基准表现，能够获得 10.4% 的显著提高。在 AISHELL-1 测试集中我们能够达到 3.31% 的 CER，代表了我们目前的最佳结果，而在监督的 1000 小时 AISHELL-2 数据集上，则能够达到 4.73% 的 CER，具有竞争性的表现。

改进非目标域数据上嘈杂学生训练以提高自动语音识别能力

Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition

To improve low-resource Neural Machine Translation (NMT) with multilingual
corpora, training on the most related high-resource language only is often more
effective than using all data available (Neubig and Hu, 2018). However, it is
possible that an intelligent data selection strategy can further improve
low-resource NMT with data from other auxiliary languages. In this paper, we
seek to construct a sampling distribution over all multilingual data, so that
it minimizes the training loss of the low-resource language. Based on this
formulation, we propose an efficient algorithm, Target Conditioned Sampling
(TCS), which first samples a target sentence, and then conditionally samples
its source sentence. Experiments show that TCS brings significant gains of up
to 2 BLEU on three of four languages we test, with minimal training overhead.

本研究提出了一种名为目标条件采样（TCS）的有效算法，该算法基于构建一个覆盖所有多语数据的采样分布，从而最小化低资源语言的训练损失。实验结果表明，TCS 可显著提高三种测试语言的 BLEU 值，最多可达 2，同时训练开销极小。