TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models

We propose a robust and reliable evaluation metric for generative models by introducing topological and statistical treatments for rigorous support estimation. Existing metrics, such as Inception Score (IS), Frechet Inception Distance (FID), and the variants of Precision and Recall (P&R), heavily rely on supports that are estimated from sample features. However, the reliability of their estimation has not been seriously discussed (and overlooked) even though the quality of the evaluation entirely depends on it. In this paper, we propose Topological Precision and Recall (TopP&R, pronounced 'topper'), which provides a systematic approach to estimating supports, retaining only topologically and statistically important features with a certain level of confidence. This not only makes TopP&R strong for noisy features, but also provides statistical consistency. Our theoretical and experimental results show that TopP&R is robust to outliers and non-independent and identically distributed (Non-IID) perturbations, while accurately capturing the true trend of change in samples. To the best of our knowledge, this is the first evaluation metric focused on the robust estimation of the support and provides its statistical consistency under noise.Comment: Accepted to NeurIPS 202

Sirolimus-eluting stent is superior to paclitaxel-eluting stent for coronary intervention in patients with renal insufficiency: Long-term clinical outcomes

Background: Renal insufficiency (RI) is an independent risk factor for the adverse cardiovascular events. Long-term clinical outcome of percutaneous coronary intervention (PCI) in patients with RI is unknown especially in the era of first generation drug-eluting stents (DES). This study aims at comparing clinical outcomes between sirolimus-eluting stents (SES) and paclitaxel-eluting stents (PES) based on large scaled registry.Methods: Patients who underwent PCI with DES from January 2004 to December 2009 in the Catholic University of Korea-PCI (COACT) registry were prospectively enrolled. A group of 1,033 patients with RI, defined as estimated glomerular filtration rate under 60 mL/min, were analyzed. Major adverse cardiac events (MACE), including all-cause death, non-fatal myocardial infarction (MI), target lesion revascularization (TLR), and target vessel revascularization (TVR) according to the type of stents were compared.Results: Median follow-up period was 810 days (interquartile range: from 361 to 1,354 days). A group of 612 (59.2%) patients were treated with SES and 421 (40.8%) patients were treated with PES. The PES vs. SES group had significantly higher rate of MACE (35.9% vs. 28.3%, p = 0.01). In multivariate Cox hazard regression analysis, PES vs. SES group had significantly higher rate of MACE (adjusted hazard ratio [AHR] 1.29, 95% confidence interval [CI] 1.02–1.64, p = 0.033), particularly pronounced by all-cause death (AHR 1.34, 95% CI 1.008–1.770; p = 0.044). In further analysis with propensity score matching, overall findings were consistent.Conclusions: In patients with RI, PCI using PES provides poorer clinical outcomes than SES in terms of MACE and all-cause death

Two Cases of Percutaneous Intervention for Coronary Artery Bypass Graft Anastomoses With Paclitaxel-Eluting Balloon Catheters

Coronary artery bypass graft (CABG) intervention, particularly anastomosis site intervention, is challenging for interventional cardiologists. A paclitaxel-eluting balloon catheter (SeQuent Please) is a recently-introduced device capable of delivering paclitaxel homogeneously into the targeted vessel wall. We herein report our experience with two cases. In the first case, coronary angiography showed significant stenosis at the site of anastomosis between the saphenous vein graft and the left anterior descending artery (LAD). In the second case, coronary angiography showed significant stenosis at the site of anastomosis between the left internal mammary artery and the LAD. We performed percutaneous intervention of these CABG anastomoses using paclitaxel-eluting balloon catheters, and obtained favorable angiographic and clinical outcomes

Indirect Volume Estimation for Acute Ischemic Stroke from Diffusion Weighted Image Using Slice Image Segmentation

The accurate estimation of acute ischemic stroke (AIS) using diffusion-weighted imaging (DWI) is crucial for assessing patients and guiding treatment options. This study aimed to propose a method that estimates AIS volume in DWI objectively, quickly, and accurately. We used a dataset of DWI with AIS, including 2159 participants (1179 for internal validation and 980 for external validation) with various types of AIS. We constructed algorithms using 3D segmentation (direct estimation) and 2D segmentation (indirect estimation) and compared their performances with those annotated by neurologists. The proposed pretrained indirect model demonstrated higher segmentation performance than the direct model, with a sensitivity, specificity, F1-score, and Jaccard index of 75.0%, 77.9%, 76.0, and 62.1%, respectively, for internal validation, and 72.8%, 84.3%, 77.2, and 63.8%, respectively, for external validation. Volume estimation was more reliable for the indirect model, with 93.3% volume similarity (VS), 0.797 mean absolute error (MAE) for internal validation, VS of 89.2% and a MAE of 2.5% for external validation. These results suggest that the indirect model using 2D segmentation developed in this study can provide an accurate estimation of volume from DWI of AIS and may serve as a supporting tool to help physicians make crucial clinical decisions

Indirect Volume Estimation for Acute Ischemic Stroke from Diffusion Weighted Image Using Slice Image Segmentation

The accurate estimation of acute ischemic stroke (AIS) using diffusion-weighted imaging (DWI) is crucial for assessing patients and guiding treatment options. This study aimed to propose a method that estimates AIS volume in DWI objectively, quickly, and accurately. We used a dataset of DWI with AIS, including 2159 participants (1179 for internal validation and 980 for external validation) with various types of AIS. We constructed algorithms using 3D segmentation (direct estimation) and 2D segmentation (indirect estimation) and compared their performances with those annotated by neurologists. The proposed pretrained indirect model demonstrated higher segmentation performance than the direct model, with a sensitivity, specificity, F1-score, and Jaccard index of 75.0%, 77.9%, 76.0, and 62.1%, respectively, for internal validation, and 72.8%, 84.3%, 77.2, and 63.8%, respectively, for external validation. Volume estimation was more reliable for the indirect model, with 93.3% volume similarity (VS), 0.797 mean absolute error (MAE) for internal validation, VS of 89.2% and a MAE of 2.5% for external validation. These results suggest that the indirect model using 2D segmentation developed in this study can provide an accurate estimation of volume from DWI of AIS and may serve as a supporting tool to help physicians make crucial clinical decisions

Another Look at Obesity Paradox in Acute Ischemic Stroke: Association Rule Mining

Though obesity is generally associated with the development of cardiovascular disease (CVD) risk factors, previous reports have also reported that obesity has a beneficial effect on CVD outcomes. We aimed to verify the existing obesity paradox through binary logistic regression (BLR) and clarify the paradox via association rule mining (ARM). Patients with acute ischemic stroke (AIS) were assessed for their 3-month functional outcome using the modified Rankin Scale (mRS) score. Predictors for poor outcome (mRS 3–6) were analyzed through BLR, and ARM was performed to find out which combination of risk factors was concurrently associated with good outcomes using maximal support, confidence, and lift values. Among 2580 patients with AIS, being obese (OR [odds ratio], 0.78; 95% CI, 0.62–0.99) had beneficial effects on the outcome at 3 months in BLR analysis. In addition, the ARM algorithm showed obese patients with good outcomes were also associated with an age less than 55 years and mild stroke severity. While BLR analysis showed a beneficial effect of obesity on stroke outcome, in ARM analysis, obese patients had a relatively good combination of risk factor profiles compared to normal BMI patients. These results may partially explain the obesity paradox phenomenon in AIS patients