Miracle-dz/UniFAR-FTUs huggingface dataset:
ReINTEL Challenge 2020: A Comparative Study of Hybrid Deep Neural Network for Reliable Intelligence Identification on Vietnamese SNSs

The overwhelming abundance of data has created a misinformation crisis. Unverified sensationalism that is designed to grab the readers' short attention span, when crafted with malice, has caused irreparable damage to our society's structure. As a result, determining the reliability of an article has become a crucial task. After various ablation studies, we propose a multi-input model that can effectively leverage both tabular metadata and post content for the task. Applying state-of-the-art finetuning techniques for the pretrained component and training strategies for our complete model, we have achieved a 0.9462 ROC-score on the VLSP private test set.
Overview of VLSP RelEx shared task: A Data Challenge for Semantic Relation Extraction from Vietnamese News

This paper reports the overview of RelEx shared task for semantic relation extraction from Vietnamese News, which is hosted at the seventh annual workshop on Vietnamese Language and Speech Processing (VLSP 2020). This task focuses on classifying entity pairs in Vietnamese News text into four different, non-overlapping categories of semantic relations defined in advance. In order to generate a fair benchmark, we build a human-annotated dataset of 1,056 documents and 5,900 instances of semantic relations, collected from Vietnamese News in several domains. All models will be evaluated in terms of macro-and micro-averaged F1 scores, two typical evaluation metrics for semantic relation extraction problem.
Vietnamese-English Translation with Transformer and Back Translation in VLSP 2020 Machine Translation Shared Task

Transformers have been proven to be more effective for machine translation and many NLP tasks. However, those networks may not work well to low-resource translation tasks, such as the one for the English-Vietnamese language pair. Therefore, this paper aims to enhance the quality of the machine translation model by using the transformer model with a back-translation technique. An intermediate translation system was built using the bilingual dataset as a training corpus. This system was then used with a large monolingual dataset to generate the back-translation data, which can be considered as augmented training data for the translation model. The experimental result on the IWSLT’15 English-Vietnamese test set showed that the system with back-translation outperforms about 2.4 BLEU points than the system with the only transformer. With the test set of the Machine Translation shared task in VLSP 2020, the proposed system with back-translation was ranked as the first place with the highest score of human evaluation (1.55 points, compared to 1.33 points for second place). With the automatic evaluation, the sys-tem achieved a 32.1 BLEU score and a 0.50 TER score on VLSP 2020 Machine translation task test data.
The UET-ICTU Submissions to the VLSP 2020 News Translation Task

Our UET-ICTU team includes members from the University of Engineering and Technology (UET) and Thai Nguyen University of Information and Communication Technology (ICTU). We participate in the VLSP 2020 Shared Task for Machine Translation which focuses on the news domain translation in one direction English → Vietnamese. Our neural machine translation (NMT) system uses Back Translation (BT) of monolingual data in the target language to augment synthetic training data. Besides, we leverage the Term Frequency and Inverse Document Frequency (TF-IDF) method to data selection close to the in-domain from other monolingual and parallel resources. To enhance the effectiveness of the system translation, we also employ other techniques such as ﬁne-tuning and assembly translation. Our experiments showed that the sys-tem can achieve a signiﬁcant improvement in BLEU score up to + 16.57 overcoming the in-domain baseline system.
Blockade of IL-7 signaling suppresses inflammatory responses and reverses alopecia areata in C3H/HeJ mice

Blocking the overexpression of IL-7 in lesional alopecis skin can reverse alopecia areata in C3H/HeJ mice. The interleukin-7 (IL-7) signaling pathway plays an important role in regulation of T cell function and survival. We detected overexpression of IL-7 in lesional skin from both humans and C3H/HeJ mice with alopecia areata (AA), a T cell–mediated autoimmune disease of the hair follicle. We found that exogenous IL-7 accelerated the onset of AA by augmenting the expansion of alopecic T cells. Conversely, blockade of IL-7 stopped the progression of AA and reversed early AA in C3H/HeJ mice. Mechanistically, we observed that IL-7Rα blockade substantially reduced the total number of most T cell subsets, but relative sparing of regulatory T cells (Tregs). We postulated that short-term anti–IL-7Rα treatment in combination with a low dose of Treg-tropic cytokines might improve therapeutic efficacy in AA. We demonstrated that short-term IL-7Rα blockade in combination with low doses of Treg-tropic cytokines enhanced therapeutic effects in the treatment of AA, and invite further clinical investigation.
Mechanisms associated with increased physical activity in patients undergoing self-management behaviour modification in the randomised PHYSACTO trial

Introduction In this analysis of the PHYSACTO® study, we assessed the efficacy of a self-management behaviour modification (SMBM) programme to improve physical activity (PA) levels, and the extent to which effects were mediated by readiness to change, motivation and confidence. Methods PHYSACTO® was a randomised, partially double-blind, parallel-group, 12-week trial to evaluate the effects of treatment on exercise capacity and PA. COPD patients received placebo, tiotropium 5 µg or tiotropium/olodaterol 5/5 µg, with or without exercise training, all with an SMBM intervention (the Living Well with COPD programme). Changes were assessed in readiness to change (stage of change visual analogue scale [VAS]), motivation (Treatment Self-Regulation Questionnaire [TSRQ]) and confidence (Perceived Competence Scale [PCS]) to engage in PA. Results PA was increased in all patients with complete PA data at Week 12 (n=262; +6038 steps·week−1, p<0.001). Significant increases were observed in patients' readiness to change (VAS 0.7 [0.6–0.8]), autonomous regulation (TRSQ 0.2 [0.1–0.3]) and confidence (PCS 0.5 [0.3–0.6]) (all p<0.01). Of note, 23% of the total effect of SMBM on steps·week−1 was found to be mediated by increases in readiness to change, 5% by TSRQ autonomous regulation and 12% by PCS. Conclusion Our study demonstrated that an SMBM programme delivered to COPD patients increased PA, mediated by an improvement of three key hypothesised mechanisms of change: readiness to change, autonomous motivation and confidence. For the first time, this study shows that an SMBM programme can be successful in altering the mechanisms of change targeted by the intervention. Self-management improves motivation and confidence to engage in physical activity in COPD patients in PHYSACTO https://bit.ly/2MKoPSy
Ring artifact removal method with generative adversarial network for the refraction-contrast computed tomography

Refraction-contrast X-ray computed tomography (RCT), which provides a high-contrast image of biological soft tissues, has recently been used in pathology and micro anatomy research. One of the characteristics of RCT is that the projection obtained by RCT system represents the first differentiation of the object's radon transformation. Therefore, the reconstruction of RCT involves an integration process of the projection before conventional back-projection. However, this process causes noise generated by the detector or refraction angle estimation errors to propagate throughout the projection image, which leads to increase ring artifacts (RA) on the reconstructed image. Although various methods for RA removal have been developed in the absorption-based CT (ACT) field, it is difficult to eliminate surface widespread RA on RCT image. In this research, we propose a RA removal method based on conditional generative adversarial network (cGAN) for RCT. This method incorporates a sparse property of Laplacian RCT image which is easily obtained by reconstructing the differential projection. To demonstrate the effectiveness of this method, we applied the proposed and conventional ACT-based methods to simulation data of numerical phantoms and compared them by the root mean square error and the structural similarity. In the result, we demonstrated the proposed method can remove RA better than conventional methods.
Domain adaptation for organ segmentation from non-contrast to contrast enhanced CT

Artificial intelligence (AI) models are used in medical image processing and analysis tasks like organ segmentation, anomaly detection, image reconstruction, and so on. Most often these models are trained on specific type of source domain images (non-contrast or contrast enhanced, specific field-of-view (FOV), dosage, demography, etc). It is desirable to adapt these models to a different but similar target domain, through unsupervised or semi-supervised learning methods. This paper describes a framework to re-purpose trained organ segmentation models for a target domain on which the model was not trained. The adaptation is proposed using an additional autoencoder network as a post-processing step to improve accuracy of predicted segmentation mask. Unsupervised and semi-supervised versions of adaptation are tested on contrast enhanced Computed Tomography (CT) liver, cardiac and lung exams. Experiment results show adaptability of a trained network from non-contrast to contrast enhanced scans with improved accuracy in contrast enhanced volume segmentation. A domain adaptation case study on lung disease exams with bacterial pneumonia or COVID-19 pathology also shows the effectiveness of proposed methodology.
Gland segmentation in pancreas histopathology images based on selective multi-scale attention

Pathology is an important subject in the treatment of pancreatic cancer. The tumor presented in the pathological images includes not only the tumor cells, but also the surrounding background structures. Automatic and accurate gland segmentation in histopathology images plays a significant role for cancer diagnosis and clinical application, which assist pathologists to diagnose the malignancy degree of pancreas caner. Due to the large variability of size and shape in glandular appearance and the heterogeneity between different cells, it is a challenging task to accurately segment glands in histopathology images. In this paper, a selective multi-scale attention (SMA) block is proposed for gland segmentation. First, a selection unit is used between the encoder and decoder to select features by amplifying effective information and suppressing redundant information according to a factor obtained during training. Second, we propose a multi-scale attention module to fuse feature maps at different scales. Our method is validated on a dataset of 200 images of size 512×512 from 24 H&E stained pancreas histological images. Experimental results show that our method achieves more accurate segmentation results than that of state-of-the-art approaches.
Keypoint matching networks for longitudinal fundus image affine registration

Registration of retinal images is an important technique for facilitating the diagnosis and treatment of many eye diseases. Recent studies have shown that deep learning methods can be used for image registration, which is usually faster than conventional registration methods. However, it is not trivial to obtain ground truth for supervised methods and popular unsupervised methods perform not well for retinal images. Therefore, we present a weakly-supervised learning method for affine registration of fundus image. The framework consists of multiple steps, rigid registration, overlap calculation and affine registration. In addition, we introduce a keypoint matching loss to replace common similarity metrics loss used in unsupervised methods. On a fundus image dataset related to multiple eye diseases, our framework can achieve more accurate registration results than that of state-of-the-art deep learning approaches.
Accurate estimation of total intracranial volume in MRI using a multi-tasked image-to-image translation network

Total intracranial volume (TIV) is the volume enclosed inside the cranium, inclusive of the meninges and the brain. TIV is extensively used to correct variations in inter-subject head size for the evaluation of neurodegen- erative diseases. In this work, we present an automatic method to generate a TIV mask from MR images while synthesizing a CT image to be used in subsequent analysis. In addition, we propose an alternative way to obtain ground truth TIV masks using a semi-manual approach, which results in significant time savings. We train a conditional generative adversarial network (cGAN) using 2D MR slices to realize our tasks. The quantitative evaluation showed that the model was able to synthesize CT and generate TIV masks that closely approximate the reference images. This study also provides a comparison of the described method against skull stripping tools that output a mask enclosing the cranial volume, using MRI scan. In particular, highlighting the deficiencies in using such tools to approximate the volume using MRI scan.
Comparison between threshold-based and deep learning-based bone segmentation on whole-body CT images

Objectives: Bone segmentation can help bone disease diagnosis or post treatment assessment but manual segmentation is a time consuming and tedious task in clinical practice. In this work, three automatic methods to segment bone structures on whole body CT images were compared. Methods: A threshold-based approach with morphological operations and two deep learning methods using a 3D U-Net with different losses, one with a cross entropy/Dice loss and the second with a Hausdorff Distance/Dice loss, were developed. Ground truth bone segmentations were generated by manually correcting the results obtained with the threshold based method. The automatic bone segmentations were evaluated using a Dice score and Hausdorff distance. Visual evaluation was also performed by a medical expert. Results: Dice scores of 0.953, 0.986 and 0.978 were achieved for the Threshold-based method and the two deep learning methods, respectively. Visual evaluation showed that the deep learning method with a Hausdorff Distance/Dice loss performed the best.
Effect of surface temperature on GeSbTe damage formation during plasma processing

Phase Change Memory (PCM) materials can be damaged during plasma exposure leading to changes in phase transition behavior. Etch-induced damage and crystallization properties of GeSbTe (GST) were evaluated as a function of substrate temperature, plasma chemistry, and plasma exposure time. Enhanced damage formation is related to selective elemental depletion and non-volatilized etch residue retention in the near surface region. These experiments validate literature findings that crystallization time increases with reduction in film thickness for GST samples capped with a thin SiO2 film, indicating the presence of a modified layer which serves as an interface layer material. A direct comparison of passivating properties of hydrofluorocarbon and hydrocarbon on GST can be more conclusive with a fine tuning of film thickness and an evaluation of total residue retention with depth profiling.
Joint optic disc and cup segmentation based on multi-module U-shaped network

Glaucoma is a leading cause of irreversible blindness. Accurate optic disc (OD) and optic cup (OC) segmentation in fundus images is beneficial to glaucoma screening and diagnosis. Recently, convolutional neural networks have demonstrated promising progress in OD and OC joint segmentation in fundus images. However, the segmentation of OC is a challenge due to the low contrast and blurred boundary. In this paper, we propose an improved U-shape based network to jointly segment OD and OC. There are three main contributions: (1) The efficient channel attention (ECA) blocks are embedded into our proposed network to avoid dimensionality reduction and capture cross-channel interaction in an efficient way. (2) A multiplexed dilation convolution (MDC) module is proposed to extract more target features with various sizes and preserve more spatial information. (3) Three global context extraction (GCE) modules are used in our network. By introducing multiple GCE modules between encoder and decoder, the global semantic information flow from high-level stages can be gradually guided to different stages. The method proposed in this paper was tested on 240 fundus images. Compared with U-Net, Attention U-Net, Seg-Net and FCNs, the OD and OC’s mean Dice similarity coefficient of the proposed method can reach 96.20% and 90.00% respectively, which are better than the above networks.
Deep learning-based lesion segmentation in paediatric epilepsy

In this research, our goal is to implement a Convolutional Neural Network (CNN) to segment Focal Cortical Dysplasia (FCD). FCD is a common lesion responsible for paediatric medically intractable focal epilepsy. MRI features of FCD can be subtle and may be missed by a radiologist. Recent advances in deep learning techniques in different fields have motivated us to develop a deep learning-based model to detect and segment the lesion responsible for FCD. We proposed a Fully Convolutional Network (FCN) for the task of FCD detection and localization. Our proposed model has four blocks of two convolutional layers followed by a pooling layer, as feature extraction part. Then, we have added three up-sampling blocks which include one convolutional layer and one up-sampling layer. The convolutional layers’ kernels are 3×3 and we are utilizing 4x and 2x upsampling layers in the decoder part. We are using skip layers as well, to get a more refined up-sampled output. We are adding the respective down-sampled feature map from the encoder part. To train and evaluate the model leaveone-out technique has been utilized where one test subject is left out of training in each experiment. We have identified 13 out 13 healthy subjects as healthy. The model has identified the lesion in 15 out of 17 MR-positive FCD subjects with 73 percent lesion coverage. For MR-negative cases, 11 out of 13 subjects were identified with lesion coverage of 64 percent. Based on our experiments, FCN holds the potential to assist specialists in detecting and localizing the lesion.
Diffusion MRI and silver standard masks to improve CNN-based thalamus segmentation

The thalamus is an internal structure of the brain whose changes are related to diseases such as multiple sclerosis and Parkinson’s disease. Thus, the thalamus segmentation is an important step in studies and applications related to these disorders, for example, for shape measuring and surgical planning. The most used software and tools for brain structures segmentation employ atlas-based algorithms that usually require long processing times and sometimes lead to inaccurate results on sub-cortical structures. New methods, that minimize those problems, using deep learning for segmenting brain structures have been recently proposed. However, for some structures such as the thalamus, these methods still tend to have unsatisfactory results since they rely only on T1w images, where the contrast can be low or absent. Aiming to overcome these issues, we proposed a Convolutional Neural Network (CNN) trained with multi-modal data (structural and diffusion MRI) and the use of silver standard masks created from multiple automatic segmentations. Results on a subset of 190 subjects from the Human Connectome Project (HCP) showed an improvement in segmentation quality, confirming the effectiveness of diffusion data in differentiating tissues due to measured micro-structural properties.
Optical coherence tomography and Raman spectroscopy for ex vivo characterization of canine and feline skin and subcutaneous tumors: preliminary results

Cancer is one of the leading causes of companion animal mortality. Up to 30% of all canine and feline tumors appear on or directly under the skin. To date, only a limited number of studies applied biophotonics techniques for optical characterization and detection of tumors in pets. In this work, we acquired ex vivo optical coherence tomography (OCT) images and Raman spectra of native skin and the most common canine and feline skin and subcutaneous tumors; lipomas, mast cell tumors, and soft tissues sarcomas. Lipomas exhibited the most distinctive tissue morphology (i.e., honeycomb structure) and biochemistry (lipid-related Raman peaks of 1063, 1301, and 1652 cm-1). Moreover, lipomas had significantly higher values of coefficient of variation (CV) retrieved from OCT images. On the other hand, all other tissues exhibited signal-dense and highly scattering OCT images. Despite the similar Raman spectra, we detected the malignant tumors with the sensitivity and specificity of 100% and 88.2%, respectively. Additionally, malignant tumor types were distinguished with an accuracy of 78.6%. Our results showed the potential of OCT and Raman techniques for ex vivo optical characterization of common canine and feline tumors and native skin.
Super-resolution and deblurring enhancement for narrow band imaging bronchoscopy

Narrow Band Imaging (NBI) is a relatively new endoscopic imaging modality that provides enhanced views of the blood vessels situated near the surface of the airway walls. Certain vessel patterns have been shown to be potential indications of cancerous lesions developing in the airways. Some recent e orts have strived to use NBI bronchoscopy to locate such vessel patterns. To find these patterns, the physician is forced to navigate the bronchoscope through the airways and manually observe potential mucosal (airway wall) vascular patterns. Unfortunately, the bronchoscopic video is often degraded by motion blur and other artifacts, thereby making an already tedious search more impractical. In addition, the degraded video can decrease the potential performance of automated vascular analysis methods. We propose to exploit the richness of the highly informative video sequence by exploring methods for super-resolution video reconstruction and image deblurring|methods which have seen spectacular success in other domains for obtaining high-resolution (HR) images from a sequence of low-resolution (LR) images. Through data derived from NBI video sequences of lung-cancer patients, we demonstrate that such methods can both substantially improve the image quality of images depicting NBI-based vascular patterns and also quantitatively enable more effective automatic segmentation of the vascular patterns.