Label-Aware Dual Graph Neural Networks for Multi-Label Fundus Image Classification.

Fundus disease is a complex and universal disease involving a variety of pathologies. Its early diagnosis using fundus images can effectively prevent further diseases and provide targeted treatment plans for patients. Recent deep learning models for classification of this disease are gradually emerging as a critical research field, which is attracting widespread attention.

Graph Neural Networks With Adaptive Confidence Discrimination.

Graph neural networks (GNNs) have demonstrated remarkable success for semisupervised node classification. However, these GNNs are still limited to the conventionally semisupervised framework and cannot fully leverage the potential value of large numbers of unlabeled samples. The pseudolabeling method in semisupervised learning (SSL) is widely recognized because it can clearly leverage unlabeled samples.

Digital phase-shift method based on distance mapping for phase recovery of an ESPI fringe pattern.

In view of the limitation of the traditional method to recover the phase of the single fringe pattern, we propose a digital phase-shift method based on distance mapping for phase recovery of an electronic speckle pattern interferometry fringe pattern. First, the direction of each pixel point and the centerline of the dark fringe are extracted. Secondly, the normal curve of the fringe is calculated according to the fringe orientation to obtain the fringe moving direction.

HGBER: Heterogeneous Graph Neural Network With Bidirectional Encoding Representation.

Heterogeneous graphs with multiple types of nodes and link relationships are ubiquitous in many real-world applications. Heterogeneous graph neural networks (HGNNs) as an efficient technique have shown superior capacity of dealing with heterogeneous graphs. Existing HGNNs usually define multiple meta-paths in a heterogeneous graph to capture the composite relations and guide neighbor selection.

RNN-combined graph convolutional network with multi-feature fusion for tuberculosis cavity segmentation.

Tuberculosis is a common infectious disease in the world. Tuberculosis cavities are common and an important imaging signs in tuberculosis. Accurate segmentation of tuberculosis cavities has practical significance for indicating the activity of lesions and guiding clinical treatment.

Joint segmentation of retinal layers and macular edema in optical coherence tomography scans based on RLMENet.

Purpose: The segmentation of retinal layers and fluid lesions on retinal optical coherence tomography (OCT) images is an important component of screening and diagnosing retinopathy in clinical ophthalmic treatment. We designed a novel network for accurate segmentation of the seven tissue layers of the retina and lesion areas of diabetic macular edema (DME), which can assist doctors to quantitatively analyze the disease.

Methods: In this article, we propose the Retinal Layer Macular Edema Network (RLMENet) model to achieve end-to-end joint segmentation of retinal layers and fluids.

Pathological Voice Detection and Classification Based on Multimodal Transmission Network.

Objectives: Describing pronunciation features from multiple perspectives can help doctors accurately diagnose the pathological type of a patient's voice. According to the two modal information of sound signal and electroglottography (EGG) signal, this paper proposes a pathological voice detection and classification algorithm based on multimodal transmission network.

Methods: Firstly, we used the short-time Fourier transform (STFT) to map the features of the two signals, and designed the Mel filter to obtain the Mel spectogram.

Structural Attention Graph Neural Network for Diagnosis and Prediction of COVID-19 Severity.

With rapid worldwide spread of Coronavirus Disease 2019 (COVID-19), jointly identifying severe COVID-19 cases from mild ones and predicting the conversion time (from mild to severe) is essential to optimize the workflow and reduce the clinician's workload. In this study, we propose a novel framework for COVID-19 diagnosis, termed as Structural Attention Graph Neural Network (SAGNN), which can combine the multi-source information including features extracted from chest CT, latent lung structural distribution, and non-imaging patient information to conduct diagnosis of COVID-19 severity and predict the conversion time from mild to severe. Specifically, we first construct a graph to incorporate structural information of the lung and adopt graph attention network to iteratively update representations of lung segments.

Artificial Intelligence-Assisted Terahertz Imaging for Rapid and Label-Free Identification of Efficient Light Formula in Laser Therapy.

Photodynamic therapy (PDT) is considered a promising noninvasive therapeutic strategy in biomedicine, especially by utilizing low-level laser therapy (LLLT) in visible and near-infrared spectra to trigger biological responses. The major challenge of PDT in applications is the complicated and time-consuming biological methodological measurements in identification of light formulas for different diseases. Here, we demonstrate a rapid and label-free identification method based on artificial intelligence (AI)-assisted terahertz imaging for efficient light formulas in LLLT of acute lung injury (ALI).

[Retracted] Knockdown of ADAM10 inhibits migration and invasion of fibroblast-like synoviocytes in rheumatoid arthritis.

Following the publication of this paper, an interested reader noted that certain of the western blotting assay data shown in Fig. 4 were strikingly similar to data appearing in different form in other articles by different authors. Owing to the fact that the contentious data in the above article had already been published elsewhere, or were already under consideration for publication, prior to its submission to , the Editor has decided that this paper should be retracted from the Journal.

Voice pathology detection and classification from speech signals and EGG signals based on a multimodal fusion method.

Automatic voice pathology detection and classification plays an important role in the diagnosis and prevention of voice disorders. To accurately describe the pronunciation characteristics of patients with dysarthria and improve the effect of pathological voice detection, this study proposes a pathological voice detection method based on a multi-modal network structure. First, speech signals and electroglottography (EGG) signals are mapped from the time domain to the frequency domain spectrogram via a short-time Fourier transform (STFT).

Incomplete multi-modal representation learning for Alzheimer's disease diagnosis.

Alzheimers disease (AD) is a complex neurodegenerative disease. Its early diagnosis and treatment have been a major concern of researchers. Currently, the multi-modality data representation learning of this disease is gradually becoming an emerging research field, attracting widespread attention.

Encoder-decoder with dense dilated spatial pyramid pooling for prostate MR images segmentation.

Automatic segmentation of prostate magnetic resonance (MR) images has great significance for the diagnosis and clinical application of prostate diseases. It faces enormous challenges because of the low contrast of the tissue boundary and the small effective area of the prostate MR images. In order to solve these problems, we propose a novel end-to-end professional network which consists of an Encoder-Decoder structure with dense dilated spatial pyramid pooling (DDSPP) for prostate segmentation based on deep learning.

Lung segmentation method with dilated convolution based on VGG-16 network.

Lung cancer has become one of the life-threatening killers. Lung disease need to be assisted by CT images taken doctor's diagnosis, and the segmented CT image of the lung parenchyma is the first step to help doctor diagnosis. For the problem of accurately segmenting the lung parenchyma, this paper proposes a segmentation method based on the combination of VGG-16 and dilated convolution.

Acoustic Field of a Linear Phased Array: A Simulation Study of Ultrasonic Circular Tube Material.

As ultrasonic wave field radiated by an ultrasonic transducer influences the results of ultrasonic nondestructive testing, simulation and emulation are widely used in nondestructive testing. In this paper, a simulation study is proposed to detect defects in a circular tube material. Firstly, the ultrasonic propagation behavior was analyzed, and a formulation of the Multi-Gaussian beam model (MGB) based on a superposition of Gaussian beams is described.

[Segmentation of retinal image vessels based on fully convolutional network with depthwise separable convolution and channel weighting].

Diseases such as diabetes and hypertension can lead to change the shape of the retinal blood vessels. Segmentation of fundus images is a key step in the process of quantitative analysis of the disease, which is instructive in the analysis and diagnosis of clinical diseases. In this paper, a method for the segmentation of retinal image vessels based on fully convolutional network (FCN) with depthwise separable convolution and channel weighting is presented.

Super resolution reconstruction for medical image based on adaptive multi-dictionary learning and structural self-similarity.

To improve the quality of the super-resolution (SR) reconstructed medical images, an improved adaptive multi-dictionary learning method is proposed, which uses the combined information of medical image itself and the natural images database. In training dictionary section, it uses the upper layer images of pyramid which are generated by the self-similarity of low resolution images. In reconstruction section, the top layer image of pyramid is taken as the initial reconstruction image, and medical image's SR reconstruction is achieved by regularization term which is the non-local structure self-similarity of the image.

Automatic non-proliferative diabetic retinopathy screening system based on color fundus image.

Background: Non-proliferative diabetic retinopathy is the early stage of diabetic retinopathy. Automatic detection of non-proliferative diabetic retinopathy is significant for clinical diagnosis, early screening and course progression of patients.

Methods: This paper introduces the design and implementation of an automatic system for screening non-proliferative diabetic retinopathy based on color fundus images.

Evidence for an Association of ERCC1 Expression and Mismatch Repair Status with Overall Survival in Colorectal Cancer Patients.

Background: The aim in this study was to determine if an association of excision repair cross-complementing group 1 (ERCC1) gene and mismatch repair (MMR) status with overall survival (OS) could be found from our analysis of a large cohort of Chinese colorectal cancer patients (CRC).

Methods: In total, 2,233 tissue samples isolated from individual CRC tumors were assessed by immunohistochemistry for the expression of ERCC1 and 4 MMR genes.

Results: The rates of proficient MMR (pMMR) and ERCC1 expression were 89.

Design of optical system for binocular fundus camera.

A non-mydriasis optical system for binocular fundus camera has been designed in this paper. It can capture two images of the same fundus retinal region from different angles at the same time, and can be used to achieve three-dimensional reconstruction of fundus. It is composed of imaging system and illumination system.

Systematic immunohistochemical screening for mismatch repair and ERCC1 gene expression from colorectal cancers in China: Clinicopathological characteristics and effects on survival.

Background: We performed a systematic screening of colorectal cancer (CRC) tissues to investigate whether mismatch repair (MMR) status and ERCC1 protein expression could be predictive of clinical outcomes for these patients following the recommendation of The Evaluation of Genomic Applications in Practice of Prevention (EGAPP).

Methods: The expression of four MMR genes and ERCC1 were assessed by immunohistochemistry (IHC) from cancer tissue samples of 2233 consecutive CRC patients.

Results: We observed that most CRC patients with a proficient MMR (pMMR) status tended to have simultaneous ERCC1 protein expression (P< 0.

Impact of age and mismatch repair status on survival in colorectal cancer.

Previous studies have suggested that deficiencies in mismatch repair genes (dMMR) often occur in patients with colorectal cancer (CRC) and contribute to disease etiology. Here, we looked for a correlation of MMR status to disease outcomes from a large number of Chinese CRC patients stratified by the age of onset of disease. A total of 2233 CRC patients were analyzed and tissue biopsies of surgically removed tumors scored for MMR gene status.

A relationship to survival is seen by combining the factors of mismatch repair status, tumor location and age of onset in colorectal cancer patients.

Background: The progression of colorectal cancer (CRC) may differ depending on the location of the tumor and the age of onset of the disease. Previous studies also suggested that the molecular basis of CRC varies with tumor location, which could affect the clinical management of patients. Therefore, we performed survival analysis looking at different age groups and mismatch repair status (MMR) of CRC patients according to primary tumor location in an attempt to identify subgroups of CRC that might help in the prognosis of disease.

Association between Ki67 Index and Clinicopathological Features in Colorectal Cancer.

Background: Conflicting results have been reported about the association between the Ki67 labeling index (Ki67-Li) and clinical outcome in patients with colorectal cancer (CRC).

Patients And Methods: Ki67 expression was assessed by immunohistochemistry (IHC) in 2,233 consecutive CRC cases.

Results: We determined 992 cases to have a low and 1,241 cases to have a high Ki67-Li (representing an approximately 44-56% breakdown in distribution between low versus high patients designated by phenotype).