Performance of models highly depend not only on the used algorithm but also the data set it was applied to. This makes the comparison of newly developed tools to previously published approaches difficult. Either researchers need to implement others' algorithms first, to establish an adequate benchmark on their data, or a direct comparison of new and old techniques is infeasible. The Ischemic Stroke Lesion Segmentation (ISLES) challenge, which has ran now consecutively for 3 years, aims to address this problem of comparability. ISLES 2016 and 2017 focused on lesion outcome prediction after ischemic stroke: By providing a uniformly pre-processed data set, researchers from all over the world could apply their algorithm directly. A total of nine teams participated in ISLES 2015, and 15 teams participated in ISLES 2016. Their performance was evaluated in a fair and transparent way to identify the state-of-the-art among all submissions. Top ranked teams almost always employed deep learning tools, which were predominately convolutional neural networks (CNNs). Despite the great efforts, lesion outcome prediction persists challenging. The annotated data set remains publicly available and new approaches can be compared directly via the online evaluation system, serving as a continuing benchmark (www.isles-challenge.org).

Estimation of high-dimensional covariance matrices is known to be a difficult problem, has many applications, and is of current interest to the larger statistics community. In many applications including so-called the “large p small n” setting, the estimate of the covariance matrix is required to be not only invertible, but also well-conditioned. Although many regularization schemes attempt to do this, none of them address the ill-conditioning problem directly. In this paper, we propose a maximum likelihood approach, with the direct goal of obtaining a well-conditioned estimator. No sparsity assumption on either the covariance matrix or its inverse are are imposed, thus making our procedure more widely applicable. We demonstrate that the proposed regularization scheme is computationally efficient, yields a type of Steinian shrinkage estimator, and has a natural Bayesian interpretation. We investigate the theoretical properties of the regularized covariance estimator comprehensively, including its regularization path, and proceed to develop an approach that adaptively determines the level of regularization that is required. Finally, we demonstrate the performance of the regularized estimator in decision-theoretic comparisons and in the financial portfolio optimization setting. The proposed approach has desirable properties, and can serve as a competitive procedure, especially when the sample size is small and when a well-conditioned estimator is required.

Highly multiplexed assays using antibody coated, fluorescent (xMap) beads are widely used to measure quantities of soluble analytes, such as cytokines and antibodies in clinical and other studies. Current analyses of these assays use methods based on standard curves that have limitations in detecting low or high abundance analytes. Here we describe SAxCyB (Significance Analysis of xMap Cytokine Beads), a method that uses fluorescence measurements of individual beads to find significant differences between experimental conditions. We show that SAxCyB outperforms conventional analysis schemes in both sensitivity (low fluorescence) and robustness (high variability) and has enabled us to find many new differentially expressed cytokines in published studies.ELISA | Luminex | algorithm | sandwich immunoassay | ANOVA I n recent years the xMap bead technology (1) has made possible high throughput analysis of various analytes, especially cytokines. These assays allow simultaneous analysis of more than 50 different cytokines in small sample volumes. The focus of the present work is on analysis of these cytokine assays. The xMap bead is the solid phase of a sandwich immunoassay. The analyte is classified through a two-color barcode embedded in the bead and the abundance of the analyte on the bead is determined by the fluorescence of the dye phycoerythrin coupled to the detection antibodies. Measured levels of fluorescence from the known cytokine dilutions are used to create standard curves. These four or five parameter logistic curves are used to estimate the concentrations of analytes given their median fluorescence intensity (MFI) values.Currently statistical analysis of xMap cytokine assays relies on repeat wells done in the assay and point estimators, usually the concentrations transformed from the MFIs, for each analyte within each well. This approach works well when a large difference exists and where coefficients of variation are fairly small. However, it is the nature of screening assays that many analytes have low fluorescence values and are therefore often reported as undetected. These undetected values lead to gaps in the assay results and frequent inaccuracies in estimates of analyte concentration.We present here a unique statistical approach for the analysis of xMap cytokine data. Given the fluorescence of individual beads, we chose not to map the observed fluorescence to the unknown concentration level, as it adds uncertainty. Instead, we focused on a direct statistical analysis of fluorescence intensities (FIs). The use of individual bead fluorescence, as opposed to any summary number, enables analysis of low signal or poor quality data and allows more power to testing differences in analytes. The methodology, which we refer to as Statistical Analysis of xMap Cytokine Beads (SAxCyB), is a linear regression model designed to find significant differences between multiple conditions (see schematic in Fig. 1A). In the model, repeat wells of a common condition are combined after adjusting differences. Conditions ...

In this paper, we propose a majorization-minimization (MM) algorithm for highdimensional fused lasso regression (FLR) suitable for parallelization using graphics processing units (GPUs). The MM algorithm is stable and flexible as it can solve the FLR problems with various types of design matrices and penalty structures within a few tens of iterations. We also show that the convergence of the proposed algorithm is guaranteed. We conduct numerical studies to compare our algorithm with other existing algorithms, demonstrating that the proposed MM algorithm is competitive in many settings including the two-dimensional FLR with arbitrary design matrices. The merit of GPU parallelization is also exhibited.

The method for eliminating misleading false intersections in 2D projections of the abdominal aortic tree conserves the overall shape and does not diminish accurate identifiability of the branches.