The widespread application of X-ray computed tomography (CT) in clinical diagnosis has led to increasing public concern regarding excessive radiation dose administered to patients. However, reducing the radiation dose will inevitably cause server noise and affect radiologists' judgment and confidence. Hence, progressive low-dose CT (LDCT) image reconstruction methods must be developed to improve image quality. Over the past two years, deep learning-based approaches have shown impressive performance in noise reduction for LDCT images. Most existing deep learning-based approaches usually require the paired training dataset which the LDCT images correspond to the normal-dose CT (NDCT) images one-to-one, but the acquisition of well-paired datasets requires multiple scans, resulting the increase of radiation dose. Therefore, well-paired datasets are not readily available. To resolve this problem, this paper proposes an unpaired LDCT image denoising network based on cycle generative adversarial networks (CycleGAN) with prior image information which does not require a one-to-one training dataset. In this method, cyclic loss, an important trick in unpaired image-to-image translation, promises to map the distribution from LDCT to NDCT by using unpaired training data. Furthermore, to guarantee the accurate correspondence of the image content between the output and NDCT, the prior information obtained from the result preprocessed using the LDCT image is integrated into the network to supervise the generation of content. Given the map of distribution through the cyclic loss and the supervision of content through the prior image loss, our proposed method can not only reduce the image noise but also retain critical information. Real-data experiments were carried out to test the performance of the proposed method. The peak signal-to-noise ratio (PSNR) improves by more than 3 dB, and the structural similarity (SSIM) increases when compared with the original CycleGAN without prior information. The real LDCT data experiment demonstrates the superiority of the proposed method according to both visual inspection and quantitative evaluation.
Pragmatic competence plays a very significant role in cross-cultural communication. In Chinese high school, many English teachers focus more on lexical and syntactic aspects of English. The aspect of pragmatics, however, is relatively neglected by high school English teachers. The aim of this research is to investigate pragmatic transfer in refusal speech act made by Chinese high school EFL (English as a Foreign Language) learners. Written DCT (Discourse Completion Test) was used for data collection. Research subjects included three groups: native Chinese speakers (NC), Chinese English learners (CE), and native English speakers (NE). The performance of three groups were compared to find out the differences of refusals made by Chinese and Americans, the characteristics of pragmatic transfer in EFL learners as well as the realtiaonship between pragmtiace transfer and L2 proficiency. Results show that 1) In terms of the frequency of semantic formulas, American speakers liked to use more direct refusal strategies and positive feelings than Chinese speakers. 2) Apparent pragmatic transfer could be found in CE1 and CE3 groups as regards to the frequency of semantic formulas. For instance, both Chinese speakers and EFL learners used address forms while no one in NE group use them. 3) A large amount of pragmatic transfer could be found in the content of refusal strategy of excuse. Statistics show that both NCs and CEs used the similar content as an excuse when giving a rejection. 4) In terms of pragmatic transfer and L2 linguisitic ability, results indicate that the overall tendency of the co-relationship is negative. More pragmatic transfer happened in CE1 group than CE3 group.
Lung cancer ranks among the most common types of cancer. Noninvasive computer-aided diagnosis can enable large-scale rapid screening of potential patients with lung cancer. Deep learning methods have already been applied for the automatic diagnosis of lung cancer in the past. Due to restrictions caused by single modality images of dataset as well as the lack of approaches that allow for a reliable extraction of fine-grained features from different imaging modalities, research regarding the automated diagnosis of lung cancer based on noninvasive clinical images requires further study. In this paper, we present a deep learning architecture that combines the fine-grained feature from PET and CT images that allow for the noninvasive diagnosis of lung cancer. The multidimensional (regarding the channel as well as spatial dimensions) attention mechanism is used to effectively reduce feature noise when extracting fine-grained features from each imaging modality. We conduct a comparative analysis of the two aspects of feature fusion and attention mechanism through quantitative evaluation metrics and the visualization of deep learning process. In our experiments, we obtained an area under the ROC curve of 0.92 (balanced accuracy = 0.72) and a more focused network attention which shows the effective extraction of the fine-grained feature from each imaging modality.
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