AFFnet - a deep convolutional neural network for the detection of atypical femur fractures from anteriorposterior radiographs.

Despite well-defined criteria for radiographic diagnosis of atypical femur fractures (AFFs), missed and delayed diagnosis is common. An AFF diagnostic software could provide timely AFF detection to prevent progression of incomplete or development of contralateral AFFs. In this study, we investigated the ability for an artificial intelligence (AI)-based application, using deep learning models (DLMs), particularly convolutional neural networks (CNNs), to detect AFFs from femoral radiographs.

Bridging Global Context Interactions for High-Fidelity Pluralistic Image Completion.

We introduce PICFormer, a novel framework for Pluralistic Image Completion using a transFormer based architecture, that achieves both high quality and diversity at a much faster inference speed. Our key contribution is to introduce a code-shared codebook learning using a restrictive CNN on small and non-overlapping receptive fields (RFs) for the local visible token representation. This results in a compact yet expressive discrete representation, facilitating efficient modeling of global visible context relations by the transformer.

Clinical phenotypes of adult-onset Behçet's syndrome: a comprehensive cross-sectional study in China.

Behçet's syndrome (BS) is a variant vasculitis that can involve multiple organs with inflammatory manifestations. This study aimed to provide a more comprehensive analysis of the clinical phenotypes and characteristics of BS patients. We enrolled 2792 BS patients referred from China nationwide to Huadong Hospital Affiliated to Fudan University from October 2012 to December 2022.

PLK1-activating IFI16-STING-TBK1 pathway induces apoptosis of intestinal epithelial cells in patients with intestinal Behçet's syndrome.

Inflammatory signals from immunological cells may cause damage to intestinal epithelial cells (IECs), resulting in intestinal inflammation and tissue impairment. Interferon-γ-inducible protein 16 (IFI16) was reported to be involved in the pathogenesis of Behçet's syndrome (BS). This study aimed to investigate how inflammatory cytokines released by immunological cells and IFI16 participate in the pathogenesis of intestinal BS.

Clinical heterogeneity and five phenotypes identified in pediatric Behçet's syndrome: a cohort study from Shanghai Behçet's syndrome database.

Objectives: Behçet's syndrome (BS) is a rare disease of unknown etiology, with limited reports especially in pediatric BS. The clinical characteristics and phenotypes of pediatric BS as a highly heterogeneous variable vessel vasculitis were investigated in this study.

Methods: A cross-sectional study was conducted to compare clinical variables and descriptive characteristics of BS by age of onset and gender.

Pruning Self-Attentions Into Convolutional Layers in Single Path.

Vision Transformers (ViTs) have achieved impressive performance over various computer vision tasks. However, modeling global correlations with multi-head self-attention (MSA) layers leads to two widely recognized issues: the massive computational resource consumption and the lack of intrinsic inductive bias for modeling local visual patterns. To solve both issues, we devise a simple yet effective method named Single-Path Vision Transformer pruning (SPViT), to efficiently and automatically compress the pre-trained ViTs into compact models with proper locality added.

FastCAT: A framework for fast routing table calculation incorporating multiple protocols.

Currently, most network outages occur because of manual configuration errors. Therefore, it is essential to verify the correctness of network configurations before deployment. Computing the network control plane is a key technology for network configuration verification.

End-to-End One-Shot Human Parsing.

Previous human parsing methods are limited to parsing humans into pre-defined classes, which is inflexible for practical fashion applications that often have new fashion item classes. In this paper, we define a novel one-shot human parsing (OSHP) task that requires parsing humans into an open set of classes defined by any test example. During training, only base classes are exposed, which only overlap with part of the test-time classes.

Unifying Flow, Stereo and Depth Estimation.

We present a unified formulation and model for three motion and 3D perception tasks: optical flow, rectified stereo matching and unrectified stereo depth estimation from posed images. Unlike previous specialized architectures for each specific task, we formulate all three tasks as a unified dense correspondence matching problem, which can be solved with a single model by directly comparing feature similarities. Such a formulation calls for discriminative feature representations, which we achieve using a Transformer, in particular the cross-attention mechanism.

Facial Action Unit Detection via Adaptive Attention and Relation.

Facial action unit (AU) detection is challenging due to the difficulty in capturing correlated information from subtle and dynamic AUs. Existing methods often resort to the localization of correlated regions of AUs, in which predefining local AU attentions by correlated facial landmarks often discards essential parts, or learning global attention maps often contains irrelevant areas. Furthermore, existing relational reasoning methods often employ common patterns for all AUs while ignoring the specific way of each AU.

PCSK9 promotes T helper 1 and T helper 17 cell differentiation by activating the nuclear factor-κB pathway in ankylosing spondylitis.

Objective: Our previous study reveals that proprotein convertase subtilisin/kexin type 9 (PCSK9) is positively related to inflammatory markers, T helper (Th)-17 cells, and treatment response in ankylosing spondylitis (AS) patients. Subsequently, this study aimed to explore the effect of PCSK9 on Th cell differentiation and its potential molecular mechanism in AS.

Methods: Serum PCSK9 was determined by enzyme-linked immunosorbent assay in 20 AS patients and 20 healthy controls (HCs).

Single-Path Bit Sharing for Automatic Loss-Aware Model Compression.

Network pruning and quantization are proven to be effective ways for deep model compression. To obtain a highly compact model, most methods first perform network pruning and then conduct quantization based on the pruned model. However, this strategy may ignore that the pruning and quantization would affect each other and thus performing them separately may lead to sub-optimal performance.

Comprehensive analysis of T-cell receptor repertoires reveals antigen-driven T-cell clusters in patients with Behçet's syndrome.

T lymphocytes are the major components of adaptive immunity in Behçet's syndrome (BS) pathology. However, the precise mechanism of T-cell-induced inflammatory condition remains to be determined. We applied bulk sequencing of the T-cell receptor (TCR) β chain in peripheral blood samples from 45 patients with BS and 10 healthy donors as controls.

Serum PCSK9 is positively correlated with disease activity and Th17 cells, while its short-term decline during treatment reflects desirable outcomes in ankylosing spondylitis patients.

Objective: Proprotein convertase subtilisin/kexin type 9 (PCSK9) participates in the autoimmune disease pathology by regulating T helper (Th) cell differentiation, NF-κB pathway, toll-like receptor 4, etc. This study intended to investigate the association of serum PCSK9 with disease activity, Th cells, and treatment response in ankylosing spondylitis (AS) patients.

Methods: Eighty-nine active AS patients were enrolled in this multicenter, prospective study.

Tofacitinib as an alternative therapy for refractory intestinal Behçet's syndrome.

Background: Intestinal Behçet's syndrome is a major cause of morbidity and mortality in Behçet's syndrome.

Objectives: Current treatment challenges remain in refractory intestinal Behçet's syndrome, when patients failed first and second-line therapies.

Design: We reported the efficacy and safety profiles of tofacitinib in patients with moderate-severe intestinal Behçet's syndrome in a retrospective single-center study.

Mutual consistency learning for semi-supervised medical image segmentation.

In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation. The MC-Net+ model is motivated by the observation that deep models trained with limited annotations are prone to output highly uncertain and easily mis-classified predictions in the ambiguous regions (e.g.

Predictors of infliximab refractory intestinal Behçet's syndrome: A retrospective cohort study from the Shanghai Behçet's syndrome database.

Objectives: This retrospective cohort study aimed to find out predictors and early biomarkers of Infliximab (IFX) refractory intestinal Behçet's syndrome (intestinal BS).

Methods: We collected the baseline clinical characteristics, laboratory parameters, and concomitant therapies of intestinal BS patients treated by IFX from the Shanghai Behçet's syndrome database. After 1 year IFX therapy, intestinal BS patients with non-mucosal healing (NMH, intestinal ulcers detected by colonoscopy) and/or no clinical remission [NCR, scores of the disease activity index for intestinal Behçet's disease (DAIBD) ≥20] were defined as IFX refractory intestinal BS.

Deconfounded Image Captioning: A Causal Retrospect.

Dataset bias in vision-language tasks is becoming one of the main problems which hinders the progress of our community. Existing solutions lack a principled analysis about why modern image captioners easily collapse into dataset bias. In this paper, we present a novel perspective: Deconfounded Image Captioning (DIC), to find out the answer of this question, then retrospect modern neural image captioners, and finally propose a DIC framework: DICv1.

Image Co-Skeletonization via Co-Segmentation.

Recent advances in the joint processing of a set of images have shown its advantages over individual processing. Unlike the existing works geared towards co-segmentation or co-localization, in this article, we explore a new joint processing topic: image co-skeletonization, which is defined as joint skeleton extraction of the foreground objects in an image collection. It is well known that object skeletonization in a single natural image is challenging, because there is hardly any prior knowledge available about the object present in the image.

Cluster analysis of phenotypes of patients with Behçet's syndrome: a large cohort study from a referral center in China.

Introduction: Behcet's syndrome (BS) is a complex, heterogeneous disorder. However, classification of its subgroups is still debated. The purpose of this study was to investigate the clinical features and aggregation of patients with BS in China, based on manifestations and organ involvements.

Scalable Gaussian Process Classification With Additive Noise for Non-Gaussian Likelihoods.

Gaussian process classification (GPC) provides a flexible and powerful statistical framework describing joint distributions over function space. Conventional GPCs, however, suffer from: 1) poor scalability for big data due to the full kernel matrix and 2) intractable inference due to the non-Gaussian likelihoods. Hence, various scalable GPCs have been proposed through: 1) the sparse approximation built upon a small inducing set to reduce the time complexity and 2) the approximate inference to derive analytical evidence lower bound (ELBO).

Behçet's disease with latent infection.

Objective: The aim of this study is to examine the clinical features of patients with Behçet's disease (BD) in the presence or absence of latent tuberculosis infection (LTBI).

Methods: This was a retrospective study of 232 consecutive patients with active BD hospitalized between October 2012 and June 2017. LTBI was diagnosed based on the positive T-SPOT.

Auto-Encoding and Distilling Scene Graphs for Image Captioning.

We propose scene graph auto-encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions. Intuitively, we humans use the inductive bias to compose collocations and contextual inferences in discourse. For example, when we see the relation "a person on a bike", it is natural to replace "on" with "ride" and infer "a person riding a bike on a road" even when the "road" is not evident.

Portable Food-Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks.

Artificial scent screening systems (known as electronic noses, E-noses) have been researched extensively. A portable, automatic, and accurate, real-time E-nose requires both robust cross-reactive sensing and fingerprint pattern recognition. Few E-noses have been commercialized because they suffer from either sensing or pattern-recognition issues.