2,055 research outputs found
NETS: Extremely fast outlier detection from a data stream via set-based processing
This paper addresses the problem of efficiently detecting outliers from a data stream as old data points expire from and new data points enter the window incrementally. The proposed method is based on a newly discovered characteristic of a data stream that the change in the locations of data points in the data space is typically very insignificant. This observation has led to the finding that the existing distance-based outlier detection algorithms perform excessive unnecessary computations that are repetitive and/or canceling out the effects. Thus, in this paper, we propose a novel set-based approach to detecting outliers, whereby data points at similar locations are grouped and the detection of outliers or inliers is handled at the group level. Specifically, a new algorithm NETS is proposed to achieve a remarkable performance improvement by realizing set-based early identification of outliers or inliers and taking advantage of the net effect between expired and new data points. Additionally, NETS is capable of achieving the same efficiency even for a high-dimensional data stream through two-level dimensional filtering. Comprehensive experiments using six real-world data streams show 5 to 25 times faster processing time than state-of-the-art algorithms with comparable memory consumption. We assert that NETS opens a new possibility to real-time data stream outlier detection
Volumetric Lung Nodule Segmentation using Adaptive ROI with Multi-View Residual Learning
Accurate quantification of pulmonary nodules can greatly assist the early
diagnosis of lung cancer, which can enhance patient survival possibilities. A
number of nodule segmentation techniques have been proposed, however, all of
the existing techniques rely on radiologist 3-D volume of interest (VOI) input
or use the constant region of interest (ROI) and only investigate the presence
of nodule voxels within the given VOI. Such approaches restrain the solutions
to investigate the nodule presence outside the given VOI and also include the
redundant structures into VOI, which may lead to inaccurate nodule
segmentation. In this work, a novel semi-automated approach for 3-D
segmentation of nodule in volumetric computerized tomography (CT) lung scans
has been proposed. The proposed technique can be segregated into two stages, at
the first stage, it takes a 2-D ROI containing the nodule as input and it
performs patch-wise investigation along the axial axis with a novel adaptive
ROI strategy. The adaptive ROI algorithm enables the solution to dynamically
select the ROI for the surrounding slices to investigate the presence of nodule
using deep residual U-Net architecture. The first stage provides the initial
estimation of nodule which is further utilized to extract the VOI. At the
second stage, the extracted VOI is further investigated along the coronal and
sagittal axis with two different networks and finally, all the estimated masks
are fed into the consensus module to produce the final volumetric segmentation
of nodule. The proposed approach has been rigorously evaluated on the LIDC
dataset, which is the largest publicly available dataset. The result suggests
that the approach is significantly robust and accurate as compared to the
previous state of the art techniques.Comment: The manuscript is currently under review and copyright shall be
transferred to the publisher upon acceptanc
MEDS-Net: Self-Distilled Multi-Encoders Network with Bi-Direction Maximum Intensity projections for Lung Nodule Detection
In this study, we propose a lung nodule detection scheme which fully
incorporates the clinic workflow of radiologists. Particularly, we exploit
Bi-Directional Maximum intensity projection (MIP) images of various thicknesses
(i.e., 3, 5 and 10mm) along with a 3D patch of CT scan, consisting of 10
adjacent slices to feed into self-distillation-based Multi-Encoders Network
(MEDS-Net). The proposed architecture first condenses 3D patch input to three
channels by using a dense block which consists of dense units which effectively
examine the nodule presence from 2D axial slices. This condensed information,
along with the forward and backward MIP images, is fed to three different
encoders to learn the most meaningful representation, which is forwarded into
the decoded block at various levels. At the decoder block, we employ a
self-distillation mechanism by connecting the distillation block, which
contains five lung nodule detectors. It helps to expedite the convergence and
improves the learning ability of the proposed architecture. Finally, the
proposed scheme reduces the false positives by complementing the main detector
with auxiliary detectors. The proposed scheme has been rigorously evaluated on
888 scans of LUNA16 dataset and obtained a CPM score of 93.6\%. The results
demonstrate that incorporating of bi-direction MIP images enables MEDS-Net to
effectively distinguish nodules from surroundings which help to achieve the
sensitivity of 91.5% and 92.8% with false positives rate of 0.25 and 0.5 per
scan, respectively
Macular Gradient Measurement in Myopic Posterior Staphyloma Using Optical Coherence Tomography
Idiopathic erythrocytosis in a patient on chronic hemodialysis
AbstractA 78-year-old man on hemodialysis presented to our hospital with erythrocytosis. He had started hemodialysis 4 years previously, with a hemoglobin level of 9.8g/dL, and was administered erythropoiesis stimulating agents and ferrous sulfate. Two years previously, his hemoglobin level increased to 14.5g/dL and the treatment for anemia was discontinued. He continued hemodialysis thrice weekly; however, the hemoglobin level had increased to 17.0g/dL at the time of presenting to our hospital. His serum erythropoietin level was 31.4mIU/mL (range, 3.7–31.5mIU/mL), carboxyhemoglobin level was 0.6% (range, 0–1.5%), and oxygen saturation in ambient air was 95.4%. The JAK2 V617F mutation was not observed and other bone marrow abnormalities were not identified. The patient was diagnosed with bladder cancer and a transurethral resection was performed. Eight months after the treatment of bladder cancer, his hemoglobin level was 15.1g/dL, and he was diagnosed with idiopathic erythrocytosis
Isolated Double-Chambered Right Ventricle in a Young Adult
Double-chambered right ventricle (DCRV) is a rare congenital heart disorder in which the right ventricle is divided by an anomalous muscle bundle into a high pressure inlet portion and a low pressure outlet portion. We report a case of isolated DCRV without symptoms in adulthood, diagnosed through echocardiography, cardiac catheterization and cardiac magnetic resonance imaging
Left Ventricular Dyssynchrony in Patients Showing Diastolic Dysfunction without Overt Symptoms of Heart Failure
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