Transfer learning for establishment of recognition of COVID-19 on CT imaging using small-sized training datasets.

The coronavirus disease, called COVID-19, which is spreading fast worldwide since the end of 2019, and has become a global challenging pandemic. Until 27th May 2020, it caused more than 5.6 million individuals infected throughout the world and resulted in greater than 348,145 deaths. CT images-based classification technique has been tried to use the identification of COVID-19 with CT imaging by hospitals, which aims to minimize the possibility of virus transmission and alleviate the burden of clinicians and radiologists. Early diagnosis of COVID-19, which not only prevents the disease from spreading further but allows more reasonable allocation of limited medical resources. Therefore, CT images play an essential role in identifying cases of COVID-19 that are in great need of intensive clinical care. Unfortunately, the current public health emergency, which has caused great difficulties in collecting a large set of precise data for training neural networks. To tackle this challenge, our first thought is transfer learning, which is a technique that aims to transfer the knowledge from one or more source tasks to a target task when the latter has fewer training data. Since the training data is relatively limited, so a transfer learning-based DensNet-121 approach for the identification of COVID-19 is established. The proposed method is inspired by the precious work of predecessors such as CheXNet for identifying common Pneumonia, which was trained using the large Chest X-ray14 dataset, and the dataset contains 112,120 frontal chest X-rays of 14 different chest diseases (including Pneumonia) that are individually labeled and achieved good performance. Therefore, CheXNet as the pre-trained network was used for the target task (COVID-19 classification) by fine-tuning the network weights on the small-sized dataset in the target task. Finally, we evaluated our proposed method on the COVID-19-CT dataset. Experimentally, our method achieves state-of-the-art performance for the accuracy (ACC) and F1-score. The quantitative indicators show that the proposed method only uses a GPU can reach the best performance, up to 0.87 and 0.86, respectively, compared with some widely used and recent deep learning methods, which are helpful for COVID-19 diagnosis and patient triage. The codes used in this manuscript are publicly available on GitHub at (https://github.com/lichun0503/CT-Classification).

Triple immunosuppressive therapy in steroid-resistant nephrotic syndrome children with tacrolimus resistance or tacrolimus sensitivity but frequently relapsing.

AIM: The treatment strategy for steroid-resistant nephrotic syndrome remains uncertain at present, especially in those with calcineurin inhibitor resistance or intolerance. To date, few studies have been published using multiple combination therapy of immunosuppressive reagents for children with calcineurin inhibitor-resistant or -intolerant nephrotic syndrome. METHODS: Eighteen consecutive children with steroid- and tacrolimus (TAC)-resistant (n = 10) or TAC-sensitive but frequent relapsing nephrotic syndrome (n = 8) were randomly recruited in the present study. All of them received further triple-combination therapy by cyclophosphamide (CTX, n = 6), mycophenolate mofetil (MMF, n = 5) or leflunomide (LEF, n = 7). Their clinical data were collected and efficacy of triple-combination therapy was evaluated. RESULTS: Compared with previous double-combination therapy of prednisone (Pre) and TAC, the short-term remission rate in all 18 patients was significantly improved after the triple-combination therapy, while the frequent relapse rate in the following 12 months was also significantly decreased. Among three different subgroups with CTX, MMF or LEF therapy, no significant difference was found in short-term remission rate and the relapse rate within 1 year follow up by Kaplan-Meier plot. CONCLUSION: Triple-combination therapy with Pre + TAC + CTX/MMF/LEF is effective for short-term response and 1 year remission, without significant additional side-effects seen in children with steroid-resistant and tacrolimus-resistant or tacrolimus-sensitive but frequently relapsing nephrotic syndrome. Further study for evaluating long-term efficacy and safety of triple-combination therapy with Pre + TAC + CTX/MMF/LEF would be necessary for these patients.

Spectral analysis of the bounded linear operator in the reproducing kernel space W2(m)(D).

We first introduce some related definitions of the bounded linear operator L in the reproducing kernel space W(2)(m)(D). Then we show spectral analysis of L and derive several property theorems.

Diagnostic value of urinary exosomal miR-23b-3p, miR-30a-5p, and miR-151-3p in children with primary nephrotic syndrome.

BACKGROUND: Primary nephrotic syndrome (NS) is a common disease of the urinary system with an unclear pathogenesis. We aimed to detect the levels of urinary exosomal miR-23b-3p, miR-30a-5p, and miR-151-3p in children with primary NS, and to explore their diagnostic value for NS. METHODS: A total of 115 patients with NS who were admitted to the hospital from June 2017 to June 2019 were selected as the observation group. According to the disease progression, they were divided into an active group (acute active phase, n=68) and remission group (remission phase, n=47). In all, 50 healthy children were selected as the control group. Levels of urinary exosomal miR-23b-3p, miR-30a-5p, and miR-151-3p of each group in different periods were detected. RESULTS: The 24-h urine protein, serum albumin (ALB), and serum total cholesterol (TC) levels were significantly higher in the observation group than in the control group (P<0.05), while those in the active group were significantly higher than those in the remission group (P<0.05). The levels of miR-23b-3p and miR-30a-5p were significantly higher in the observation group than in the control group, and significantly higher in the active group than in the remission group (P<0.05). No miR-151-3p was detected in the urinary exosomes of the two groups. After treatment, levels of exosomal miR-23b-3p and miR-30a-5p in the two groups both decreased significantly (P<0.05). Results of receiver operating curve (ROC) curve analysis showed that urinary exosomal miR-23b-3p and miR-30a-5p can be used to identify children with NS and healthy children. The area under the ROC curve (AUC) was 0.711 for miR-23b-3p and 0.844 for miR-30a-5p. CONCLUSIONS: The levels of miR-23b-3p and miR-30a-5p in urinary exosomes of children with NS were significantly higher than those in healthy children, and decreased significantly after treatment, indicating that miR-23b-3p and miR-30a-5p in urinary exosomes are potential indicators for diagnosing the progression of NS and monitoring the treatment effect.

Split Bregman method based level set formulations for segmentation and correction with application to MR images and color images.

At present, magnetic resonance (MR) images have gradually become a major aid for clinical medicine, which has greatly improved the doctor's diagnosis rate. Accurate and fast segmentation of MR images plays an extremely important role in medical research. However, due to the influence of external factors and the defects of imaging devices, the MR images have severe intensity inhomogeneity, which poses a great challenge to accurately segment MR images. To deal with this problem, this paper presents an improved active contour model by combining the level set evolution model (LSE) and the split Bregman method, and gives the two-phase, the multi-phase and the vector-valued formulations of our model, respectively. The use of the split Bregman method accelerates the minimization process of our model by reducing the computation time and iterative times. A slowly varying bias field is added into the energy functional, which is the key to correct inhomogeneous images. By estimating the bias fields, not only can we get accurate image segmentation results, but also a homogeneous image after correction is provided. Then we apply our model to segment a large amount of synthetic and real MR images, including gray and color images. Experimental results show that our model can provide satisfactory segmentation and correction results for both gray and color images. Besides, compared with the LSE model, our model has higher accuracy and is superior to the LSE model. In addition, experimental results also demonstrate that our model has the advantages of being insensitive to initial contours and robust to noises.

Efficient segmentation and correction model for brain MR images with level set framework based on basis functions.

With the wide application of MR images to detect disease in human's brain deeply, the shortcomings of the technology are necessarily waiting to be solved. For example, MR images always show serious intensity inhomogeneity called the bias field, which may prevent to deduce exact analysis of images. To eliminate the distraction, many methods are proposed. Though experimental results already have stood for the advantages of those methods, there are still lots of problems that cannot be neglected, such as bad segmentation, wrong correction and over-correction which has not attracted much attention yet. Among all those methods, the multiplicative intrinsic component optimization (MICO) model influenced us more. Based on the MICO model and split Bregman method, in this paper, we put forward a new model to segment and correct bias field moderately and simultaneously for MR images. Then, we applied our model to a large quantity of MR images, and gained lots of expected results. For a better observation, we compared our model with the MICO model in both segmentation and bias correction results, it can be seen from the experimental results that our model has performed well for the challenging intensity inhomogeneity problems. Many good characteristics like accuracy, efficiency and robustness also have been exhibited in numerical results and comparisons with the MICO model.

A fast and reliable noise-resistant medical image segmentation and bias field correction model.

In recent years, with the rapid development of modern medical image technology, the medical image processing technology is becoming more important. In particular, the accurate segmentation of medical images is significant for doctors to diagnose and analyze the etiology. However, the false contours appearing in medical images due to fuzzy image boundary, intensity inhomogeneity and random noise, may lead to the inaccurate segmentation results. In this paper, an improved active contour model based on global image information is proposed, which can accurately segment images disturbed by intensity inhomogeneities and serious noise. We give the two-phase energy functional and multi-phase energy functional of our model, and apply it to segment magnetic resonance (MR) images, ultrasound (US) images and synthetic images. Experimental results and comparisons with other models have shown that our model has the advantages of higher accuracy, higher efficiency and robustness in dealing with the intensity inhomogeneity and serious noise in image segmentation.

A doubly degenerate diffusion model based on the gray level indicator for multiplicative noise removal.

Multiplicative noise removal is a challenging task in image processing. Inspired by the impressive performance of nonlinear diffusion models in additive noise removal, we address this problem in the view of nonlinear diffusion equation theories rather than the traditional variation methods. We develop a nonlinear diffusion filter denoising framework, which considers not only the information of the gradient of the image, but also the information of gray levels of the image. Furthermore, under this framework, we propose a doubly degenerate diffusion model for multiplicative noise removal, which is analyzed with respect to some of its properties and behavior in denoising process. In numerical aspects, we present an efficient scheme which uses a stabilization by fast explicit diffusion for the implementation of the multiplicative noise removal model. Finally, the experimental results illustrate effectiveness and efficiency of the proposed model.

Adaptive Perona-Malik model based on the variable exponent for image denoising.

This paper introduces a class of adaptive Perona-Malik (PM) diffusion, which combines the PM equation with the heat equation. The PM equation provides a potential algorithm for image segmentation, noise removal, edge detection, and image enhancement. However, the defect of traditional PM model is tending to cause the staircase effect and create new features in the processed image. Utilizing the edge indicator as a variable exponent, we can adaptively control the diffusion mode, which alternates between PM diffusion and Gaussian smoothing in accordance with the image feature. Computer experiments indicate that the present algorithm is very efficient for edge detection and noise removal.