Exploring Object Relation in Mean Teacher for Cross-Domain Detection

Rendering synthetic data (e.g., 3D CAD-rendered images) to generate annotations for learning deep models in vision tasks has attracted increasing attention in recent years. However, simply applying the models learnt on synthetic images may lead to high generalization error on real images due to domain shift. To address this issue, recent progress in cross-domain recognition has featured the Mean Teacher, which directly simulates unsupervised domain adaptation as semi-supervised learning. The domain gap is thus naturally bridged with consistency regularization in a teacher-student scheme. In this work, we advance this Mean Teacher paradigm to be applicable for cross-domain detection. Specifically, we present Mean Teacher with Object Relations (MTOR) that novelly remolds Mean Teacher under the backbone of Faster R-CNN by integrating the object relations into the measure of consistency cost between teacher and student modules. Technically, MTOR firstly learns relational graphs that capture similarities between pairs of regions for teacher and student respectively. The whole architecture is then optimized with three consistency regularizations: 1) region-level consistency to align the region-level predictions between teacher and student, 2) inter-graph consistency for matching the graph structures between teacher and student, and 3) intra-graph consistency to enhance the similarity between regions of same class within the graph of student. Extensive experiments are conducted on the transfers across Cityscapes, Foggy Cityscapes, and SIM10k, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, we obtain a new record of single model: 22.8% of mAP on Syn2Real detection dataset.Comment: CVPR 2019; The codes and model of our MTOR are publicly available at: https://github.com/caiqi/mean-teacher-cross-domain-detectio

Preoperative inflammatory markers predict postoperative clinical outcomes in patients undergoing heart valve surgery: A large-sample retrospective study

IntroductionPreoperative inflammation affects the postoperative outcomes of patients undergoing heart valve surgery. This study aimed to explore the role and predictive effects of preoperative inflammation on the primary outcomes after valvular cardiac surgery.MethodsThis retrospective study utilized a medical recording system to screen 5075 patients who underwent heart valve surgery. Data on the C-reactive protein (CRP) levels, erythrocyte sedimentation rate (ESR), and neutrophil-to-lymphocyte ratio (NLR) before heart valve surgery were collected from the hospital’s medical system. Postoperative hepatic insufficiency, acute kidney injury, heart failure, and myocardial damage were assessed using blood indicators. Patients with and without prolonged mechanical ventilation, extended intensive care unit stays, prolonged hospital stays, and death within 30 days after surgery (considered the primary outcome in this study) were compared. Group comparisons, receiver operating characteristic (ROC) curve analyses, and logistic analyses were performed to determine the associations between preoperative inflammation and outcomes after heart valve surgery.ResultsA total of 3249 patients were included in the analysis. Significant differences in CRP level, ESR, and NLR were found between patients with and without postoperative adverse outcomes. ROC analysis showed that CRP levels >5 mg/L effectively predicted postoperative heart failure, and NLR >3.5 had a good predictive effect on all-cause mortality within 30 days after surgery. Patients with CRP levels >5 mg/L had a higher incidence of postoperative heart failure than other patients (20.7% vs. 12.6%, P<0.001), with a relative risk of 1.447 (95% confidence interval: 1.155–1.814). Patients with NLR >3.5 had a higher incidence of death within 30 days after surgery (5.3% vs. 1.2%, P<0.001), with a relative risk of 3.236 (95% confidence interval: 1.773–5.906).ConclusionPreoperative inflammation can affect postoperative outcomes in patients undergoing heart valve surgery. CRP level >5 mg/L and NLR >3.5 can effectively predict postoperative heart failure and death within 30 days after surgery, respectively

A potential relationship between MMP-9 rs2250889 and ischemic stroke susceptibility

PurposeIschemic stroke (IS), a serious cerebrovascular disease, greatly affects people's health and life. Genetic factors are indispensable for the occurrence of IS. As a biomarker for IS, the MMP-9 gene is widely involved in the pathophysiological process of IS. This study attempts to find out the relationship between MMP-9 polymorphisms and IS susceptibility.MethodsA total of 700 IS patients and 700 healthy controls were recruited. The single nucleotide polymorphism (SNP) markers of the MMP-9 gene were genotyped by the MassARRAY analyzer. Multifactor dimensionality reduction (MDR) was applied to generate SNP–SNP interaction. Furthermore, the relationship between genetic variations (allele and genotype) of the MMP-9 gene and IS susceptibility was analyzed by calculating odds ratios (ORs) and 95% confidence intervals (CIs).ResultsOur results demonstrated that rs2250889 could significantly increase the susceptibility to IS in the codominant, dominant, overdominant, and log-additive models (p < 0.05). Further stratification analysis showed that compared with the control group, rs2250889 was associated with IS risk in different case groups (age, female, smoking, and non-drinking) (p < 0.05). Based on MDR analysis, rs2250889 was the best model for predicting IS risk (cross-validation consistency: 10/10, OR = 1.56 (1.26–1.94), p < 0.001).ConclusionOur study preliminarily confirmed that SNP rs2250889 was significantly associated with susceptibility to IS