BANet: Blur-aware Attention Networks for Dynamic Scene Deblurring

Image motion blur usually results from moving objects or camera shakes. Such blur is generally directional and non-uniform. Previous research efforts attempt to solve non-uniform blur by using self-recurrent multi-scale or multi-patch architectures accompanying with self-attention. However, using self-recurrent frameworks typically leads to a longer inference time, while inter-pixel or inter-channel self-attention may cause excessive memory usage. This paper proposes blur-aware attention networks (BANet) that accomplish accurate and efficient deblurring via a single forward pass. Our BANet utilizes region-based self-attention with multi-kernel strip pooling to disentangle blur patterns of different degrees and with cascaded parallel dilated convolution to aggregate multi-scale content features. Extensive experimental results on the GoPro and HIDE benchmarks demonstrate that the proposed BANet performs favorably against the state-of-the-art in blurred image restoration and can provide deblurred results in real-time

A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph

Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists, this paper proposes a convolutional neural network (CNN)-based regional analysis model for spical lesions for periapical radiographs. In this study, the database was provided by dentists with more than three years of practical experience, meeting the criteria for clinical practical application. The contributions of this work are (1) an advanced adaptive threshold preprocessing technique for image segmentation, which can achieve an accuracy rate of more than 96%; (2) a better and more intuitive apical lesions symptom enhancement technique; and (3) a model for apical lesions detection with an accuracy as high as 96.21%. Compared with existing state-of-the-art technology, the proposed model has improved the accuracy by more than 5%. The proposed model has successfully improved the automatic diagnosis of apical lesions. With the help of automation, dentists can focus more on technical and medical diagnoses, such as treatment, tooth cleaning, or medical communication. This proposal has been certified by the Institutional Review Board (IRB) with the certification number 202002030B0

Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database

Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over years and stored in hospitals' picture archiving and communication systems. However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxes and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates and sizes. They require little manual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. We propose algorithms for intra-patient lesion matching and missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde

Thrombomodulin Regulates Keratinocyte Differentiation and Promotes Wound Healing

The membrane glycoprotein thrombomodulin (TM) has been implicated in keratinocyte differentiation and wound healing, but its specific function remains undetermined. The epidermis-specific TM knockout mice were generated to investigate the function of TM in these biological processes. Primary cultured keratinocytes obtained from TMlox/lox; K5-Cre mice, in which TM expression was abrogated, underwent abnormal differentiation in response to calcium induction. Poor epidermal differentiation, as evidenced by downregulation of the terminal differentiation markers loricrin and filaggrin, was observed in TMlox/lox; K5-Cre mice. Silencing TM expression in human epithelial cells impaired calcium-induced extracellular signal–regulated kinase pathway activation and subsequent keratinocyte differentiation. Compared with wild-type mice, the cell spreading area and wound closure rate were lower in keratinocytes from TMlox/lox; K5-Cre mice. In addition, the lower density of neovascularization and smaller area of hyperproliferative epithelium contributed to slower wound healing in TMlox/lox; K5-Cre mice than in wild-type mice. Local administration of recombinant TM (rTM) accelerated healing rates in the TM-null skin. These data suggest that TM has a critical role in skin differentiation and wound healing. Furthermore, rTM may hold therapeutic potential for the treatment of nonhealing chronic wounds

Text-Guided Neural Image Inpainting

Image inpainting task requires filling the corrupted image with contents coherent with the context. This research field has achieved promising progress by using neural image inpainting methods. Nevertheless, there is still a critical challenge in guessing the missed content with only the context pixels. The goal of this paper is to fill the semantic information in corrupted images according to the provided descriptive text. Unique from existing text-guided image generation works, the inpainting models are required to compare the semantic content of the given text and the remaining part of the image, then find out the semantic content that should be filled for missing part. To fulfill such a task, we propose a novel inpainting model named Text-Guided Dual Attention Inpainting Network (TDANet). Firstly, a dual multimodal attention mechanism is designed to extract the explicit semantic information about the corrupted regions, which is done by comparing the descriptive text and complementary image areas through reciprocal attention. Secondly, an image-text matching loss is applied to maximize the semantic similarity of the generated image and the text. Experiments are conducted on two open datasets. Results show that the proposed TDANet model reaches new state-of-the-art on both quantitative and qualitative measures. Result analysis suggests that the generated images are consistent with the guidance text, enabling the generation of various results by providing different descriptions. Codes are available at https://github.com/idealwhite/TDANetComment: ACM MM'2020 (Oral). 9 pages, 4 tables, 7 figure

Life-threatening hemobilia caused by hepatic pseudoaneurysm after T-tube choledochostomy: report of a case

<p>Abstract</p> <p>Background</p> <p>Hemobilia is a rare but lethal biliary tract complication. There are several causes of hemobilia which might be classified as traumatic or nontraumatic. Hemobilia caused by pseudoaneurysm might result from hepatobiliary surgery or percutaneous interventional hepatobiliary procedures. However, to our knowledge, there are no previous reports pertaining to hemobilia caused by hepatic pseudoaneurysm after T-tube choledochostomy.</p> <p>Case presentation</p> <p>A 65-year-old male was admitted to our hospital because of acute calculous cholecystitis and cholangitis. He underwent cholecystectomy, choledocholithotomy via a right upper quadrant laparotomy and a temporary T-tube choledochostomy was created. However, on the 19th day after operation, he suffered from sudden onset of hematemesis and massive fresh blood drainage from the T-tube choledochostomy. Imaging studies confirmed the diagnosis of pseudoaneurysm associated hemobilia. The probable association of T-tube choledochostomy with pseudoaneurysm and hemobilia is also demonstrated. He underwent emergent selective microcoils emobolization to occlude the feeding artery of the pseudoaneurysm.</p> <p>Conclusions</p> <p>Pseudoaneurysm associated hemobilia may occur after T-tube choledochostomy. This case also highlights the importance that hemobilia should be highly suspected in a patient presenting with jaundice, right upper quadrant abdominal pain and upper gastrointestinal bleeding after liver or biliary surgery.</p

Microbiota signatures associated with invasive Candida albicans infection in the gastrointestinal tract of immunodeficient mice

Candida albicans is a commensal microorganism in the human gut but occasionally causes invasive C. albicans infection (ICA), especially in immunocompromised individuals. Early initiation of antifungal therapy is associated with reduced mortality of ICA, but rapid diagnosis remains a challenge. The ICA-associated changes in the gut microbiota can be used as diagnostic and therapeutic targets but have been poorly investigated. In this study, we utilized an immunodeficient Rag2γc (Rag2-/-il2γc-/-) mouse model to investigate the gut microbiota alterations caused by C. albicans throughout its cycle, from its introduction into the gastrointestinal tract to invasion, in the absence of antibiotics. We observed a significant increase in the abundance of Firmicutes, particularly Lachnospiraceae and Ruminococcaceae, as well as a significant decrease in the abundance of Candidatus Arthromitus in mice exposed to either the wild-type SC5314 strain or the filamentation-defective mutant (cph1/cph1 efg1/efg1) HLC54 strain of C. albicans. However, only the SC5314-infected mice developed ICA. A linear discriminate analysis of the temporal changes in the gut bacterial composition revealed Bacteroides vulgatus as a discriminative biomarker associated with SC5314-infected mice with ICA. Additionally, a positive correlation between the B. vulgatus abundance and fungal load was found, and the negative correlation between the Candidatus Arthromitus abundance and fungal load after exposure to C. albicans suggested that C. albicans might affect the differentiation of intestinal Th17 cells. Our findings reveal the influence of pathogenic C. albicans on the gut microbiota and identify the abundance of B. vulgatus as a microbiota signature associated with ICA in an immunodeficient mouse model

Dengue Virus Targets the Adaptor Protein MITA to Subvert Host Innate Immunity

Dengue is one of the most important arboviral diseases caused by infection of four serotypes of dengue virus (DEN). We found that activation of interferon regulatory factor 3 (IRF3) triggered by viral infection and by foreign DNA and RNA stimulation was blocked by DEN-encoded NS2B3 through a protease-dependent mechanism. The key adaptor protein in type I interferon pathway, human mediator of IRF3 activation (MITA) but not the murine homologue MPYS, was cleaved in cells infected with DEN-1 or DEN-2 and with expression of the enzymatically active protease NS2B3. The cleavage site of MITA was mapped to LRR↓96G and the function of MITA was suppressed by dengue protease. DEN replication was reduced with overexpression of MPYS but not with MITA, while DEN replication was enhanced by MPYS knockdown, indicating an antiviral role of MITA/MPYS against DEN infection. The involvement of MITA in DEN-triggered innate immune response was evidenced by reduction of IRF3 activation and IFN induction in cells with MITA knockdown upon DEN-2 infection. NS2B3 physically interacted with MITA, and the interaction and cleavage of MITA could be further enhanced by poly(dA:dT) stimulation. Thus, we identified MITA as a novel host target of DEN protease and provide the molecular mechanism of how DEN subverts the host innate immunity