Using a Monotonic Density Ratio Model to Find the Asymptotically Optimal Combination of Multiple Diagnostic Tests

<p>With the advent of new technology, new biomarker studies have become essential in cancer research. To achieve optimal sensitivity and specificity, one needs to combine different diagnostic tests. The celebrated Neyman–Pearson lemma enables us to use the density ratio to optimally combine different diagnostic tests. In this article, we propose a semiparametric model by directly modeling the density ratio between the diseased and nondiseased population as an unspecified monotonic nondecreasing function of a linear or nonlinear combination of multiple diagnostic tests. This method is appealing in that it is not necessary to assume separate models for the diseased and nondiseased populations. Further, the proposed method provides an asymptotically optimal way to combine multiple test results. We use a pool-adjacent-violation-algorithm to find the semiparametric maximum likelihood estimate of the receiver operating characteristic (ROC) curve. Using modern empirical process theory we show cubic root <i>n</i> consistency for the ROC curve and the underlying Euclidean parameter estimation. Extensive simulations show that the proposed method outperforms its competitors. We apply the method to two real-data applications. Supplementary materials for this article are available online.</p

Group-based atrous convolution stereo matching network

Stereo matching, is the key technology in stereo vision. Given a pair of rectified images, stereo matching determines correspondences between the pair images and estimate depth by obtaining disparity between corresponding pixels. Current work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task with an end-to-end frame based on Convolutional Neural Networks (CNNs). However, 3D CNN makes a great burden on memory storage and computation, which further leads to the significantly increased computation time. To alleviate this issue, atrous convolution was proposed to reduce the number of convolutional operations via a relatively sparse receptive field. However, this sparse receptive field makes it difficult to find reliable corresponding points in fuzzy areas, e.g., occluded areas and untextured areas, owning to the loss of rich contextual information. To address this problem, we propose Group-based Atrous Convolution Spatial Pyramid Pooling (GASPP) to robustly segment objects at multiple scales with affordable computing resources. The main feature of GASPP module is to set convolutional layers with continuous dilation rate in each group, so that it can reduce the impact of holes introduced by atrous convolution on network performance. Moreover, we introduce a tailored cascade cost volume in a pyramid form to reduce memory, so as to meet real-time performance. Group-based atrous convolution stereo matching network is evaluated on the street scene benchmark KITTI 2015 and Scene Flow and achieves state-of-the-art performance.</div

LightLog: A lightweight temporal convolutional network for log anomaly detection on the edge

Log anomaly detection on edge devices is the key to enhance edge security when deploying IoT systems. Despite the success of many newly proposed deep learning based log anomaly detection methods, handling large-scale logs on edge devices is still a bottleneck due to the limited computational power on these devices to fulfil the real-time processing requirement for accurate anomaly detection. In this work, we propose a novel lightweight log anomaly detection algorithm, named LightLog, to tackle this research gap. In specific, we achieve real-time processing speed on the task via two aspects: (i) creation of a low-dimensional semantic vector space based on word2vec and post-processing algorithms (PPA); and (ii) design of a lightweight temporal convolutional network (TCN) for the detection. These two components significantly reduce the number of parameters and computations of a standard TCN while improving the detection performance. Experimental results show that our LightLog outperforms several benchmarking methods, namely DeepLog, LogAnomaly and RobustLog, by achieving 97.0 F1 score on HDFS Dataset and 97.2 F1 score on BGL with smallest model size. This effective yet efficient method paves the way to the deployment of log anomaly detection on the edge. Our source code and datasets are freely available on https://github.com/Aquariuaa/LightLog</div

GCT-UNET: U-Net image segmentation model for a small sample of adherent bone marrow cells based on a gated channel transform module

Pathological diagnosis is considered to be declarative and authoritative. However, reading pathology slides is a challenging task. Different parts of the section are taken and read for different purposes and with different focuses, which further adds difficulty to the pathologist’s diagnosis. In recent years, the deep neural network has made great progress in the direction of computer vision and the main approach to image segmentation is the use of convolutional neural networks, through which the spatial properties of the data are captured. Among a wide variety of different network structures, one of the more representative ones is UNET with encoder and decoder structures. The biggest advantage of traditional UNET is that it can still perform well with a small number of samples, but because the information in the feature map is lost in the downsampling process of UNET, and a large amount of spatially accurate detailed information is lost in the decoding part. This makes it difficult to complete accurate segmentation of cell images with dense numbers and high adhesion. For this reason, we propose a new network structure based on UNET, which can be used to segment cell images by aggregating the global contextual information between different channels and assigning different weights to the corresponding channels through the gated adaptive mechanism, we improve the performance of UNET in the cell segmentation task and consider the use of unsupervised segmentation methods for secondary segmentation of the predicted results of our model, and the final results obtained are tested to meet the needs of the readers

GCT-UNET: U-Net image segmentation model for a small sample of adherent bone marrow cells based on a gated channel transform module

Pathological diagnosis is considered to be declarative and authoritative. However, reading pathology slides is a challenging task. Different parts of the section are taken and read for different purposes and with different focuses, which further adds difficulty to the pathologist’s diagnosis. In recent years, the deep neural network has made great progress in the direction of computer vision and the main approach to image segmentation is the use of convolutional neural networks, through which the spatial properties of the data are captured. Among a wide variety of different network structures, one of the more representative ones is UNET with encoder and decoder structures. The biggest advantage of traditional UNET is that it can still perform well with a small number of samples, but because the information in the feature map is lost in the downsampling process of UNET, and a large amount of spatially accurate detailed information is lost in the decoding part. This makes it difficult to complete accurate segmentation of cell images with dense numbers and high adhesion. For this reason, we propose a new network structure based on UNET, which can be used to segment cell images by aggregating the global contextual information between different channels and assigning different weights to the corresponding channels through the gated adaptive mechanism, we improve the performance of UNET in the cell segmentation task and consider the use of unsupervised segmentation methods for secondary segmentation of the predicted results of our model, and the final results obtained are tested to meet the needs of the readers

A few-shot learning-based Siamese capsule network for intrusion detection with imbalanced training data

Network intrusion detection remains one of the major challenges in cybersecurity. In recent years, many machine-learning-based methods have been designed to capture the dynamic and complex intrusion patterns to improve the performance of intrusion detection systems. However, two issues, including imbalanced training data and new unknown attacks, still hinder the development of a reliable network intrusion detection system. In this paper, we propose a novel few-shot learning-based Siamese capsule network to tackle the scarcity of abnormal network traffic training data and enhance the detection of unknown attacks. In specific, the well-designed deep learning network excels at capturing dynamic relationships across traffic features. In addition, an unsupervised subtype sampling scheme is seamlessly integrated with the Siamese network to improve the detection of network intrusion attacks under the circumstance of imbalanced training data. Experimental results have demonstrated that the metric learning framework is more suitable to extract subtle and distinctive features to identify both known and unknown attacks after the sampling scheme compared to other supervised learning methods. Compared to the state-of-the-art methods, our proposed method achieves superior performance to effectively detect both types of attacks.<br

Maintenance Therapy in Ovarian Cancer with Targeted Agents Improves PFS and OS: A Systematic Review and Meta-Analysis

<div><p>Background</p><p>Maintenance therapy with targeted agents for prolonging remission for ovarian cancer patients remains controversial. As a result, a meta-analysis was conducted to assess the effectiveness and safety of using maintenance therapy with targeted agents for the treatment of ovarian cancer.</p><p>Methods</p><p>From inception to January 2015, we searched for randomized, controlled trials (RCTs) using the following databases: PubMed, ScienceDirect, the Cochrane Library, Clinicaltrials.gov and EBSCO. Eligible trials included RCTs that evaluated standard chemotherapy which was either followed or not followed by targeted maintenance in patients with ovarian cancer who had been previously receiving adjunctive treatments, such as cytoreductive surgery and standard chemotherapy. The outcome measures included progression-free survival (PFS), overall survival (OS) and incidence of adverse events.</p><p>Results</p><p>A total of 13 RCTs, which were published between 2006 and 2014, were found to be in accordance with our inclusion criteria. The primary meta-analysis indicated that both PFS and OS were statistically and significantly improved in the targeted maintenance therapy group as compared to the control group (PFS: HR = 0.84, 95%CI: 0.75 to 0.95, p = 0.001; OS: HR = 0.91, 95%CI: 0.84 to 0.98, p = 0.02). When taking safety into consideration, the use of targeted agents was significantly correlated with increased risks of fatigue, diarrhea, nausea, vomiting, and hypertension. However, no significant differences were found in incidence rates of abdominal pain, constipation or joint pain.</p><p>Conclusions</p><p>Our results indicate that targeted maintenance therapy clearly improves the survival of ovarian cancer patients but may also increase the incidence of adverse events. Additional randomized, double-blind, placebo-controlled, multicenter investigations will be required on a larger cohort of patients to verify our findings.</p></div

Hazard ratios of overall survival.

<p>SE = standard error; IV = inverse variance method; CI = confidence interval.</p

Adverse events.

<p>Abbreviations: OR, odds ratio; M-H, Mantel-haenszel.</p><p>Adverse events.</p

Genetic Structure of Water Chestnut Beetle: Providing Evidence for Origin of Water Chestnut - Fig 6

<p>Potential distribution of <i>G</i>. <i>birmanica</i> under (A) current, (B) last glacial maximum (LGM) and (C) last interglacial (LIG) climate conditions. Maps were created using Esri’s ArcGIS platform (<a href="http://www.esri.com/software/arcgis" target="_blank">http://www.esri.com/software/arcgis</a>).</p