Mass detection on real and synthetic mammograms: human observer templates and local statistics

2007 ◽  
Author(s):  
Cyril Castella ◽  
Karen Kinkel ◽  
Francis R. Verdun ◽  
Miguel P. Eckstein ◽  
Craig K. Abbey ◽  
...  
Keyword(s):  
Human Observer ◽  
Mass Detection ◽  
Local Statistics

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

Using watermark visibility measurements to select an optimized pair of spot colors for use in a binary watermark

2020 ◽  
Vol 2020 (15) ◽  
pp. 197-1-197-7
Author(s):  
Alastair Reed ◽  
Vlado Kitanovski ◽  
Kristyn Falkenstern ◽  
Marius Pedersen
Keyword(s):  
Food Packaging ◽  
Red Light ◽  
Human Observer ◽  
Viewing Distance ◽  
Point Of Sale ◽  
Complementary Color ◽  
Low Visibility ◽  
Packaging Industry ◽  
Spot Color ◽  
Selection Of

Spot colors are widely used in the food packaging industry. We wish to add a watermark signal within a spot color that is readable by a Point Of Sale (POS) barcode scanner which typically has red illumination. Some spot colors such as blue, black and green reflect very little red light and are difficult to modulate with a watermark at low visibility to a human observer. The visibility measurements that have been made with the Digimarc watermark enables the selection of a complementary color to the base color which can be detected by a POS barcode scanner but is imperceptible at normal viewing distance.

scholarly journals A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jared Hamwood ◽  
Beat Schmutz ◽  
Michael J. Collins ◽  
Mark C. Allenby ◽  
David Alonso-Caneiro
Keyword(s):  
Deep Learning ◽  
Human Performance ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Human Observer ◽  
In Series ◽  
Magnetic Resonance Imaging Mri ◽  
High Degree ◽  
Orbital Region ◽  
Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

scholarly journals Search for giant planets around seven white dwarfs in the Hyades cluster with the Hubble Space Telescope

2020 ◽  
Vol 500 (3) ◽  
pp. 3920-3925
Author(s):  
Wolfgang Brandner ◽  
Hans Zinnecker ◽  
Taisiya Kopytova
Keyword(s):  
White Dwarfs ◽  
Near Infrared ◽  
Hubble Space Telescope ◽  
Infrared Camera ◽  
Detection Limits ◽  
Giant Planets ◽  
High Angular Resolution ◽  
Mass Detection ◽  
Space Telescope ◽  
Hyades Cluster

ABSTRACT Only a small number of exoplanets have been identified in stellar cluster environments. We initiated a high angular resolution direct imaging search using the Hubble Space Telescope (HST) and its Near-Infrared Camera and Multi-Object Spectrometer (NICMOS) instrument for self-luminous giant planets in orbit around seven white dwarfs in the 625 Myr old nearby (≈45 pc) Hyades cluster. The observations were obtained with Near-Infrared Camera 1 (NIC1) in the F110W and F160W filters, and encompass two HST roll angles to facilitate angular differential imaging. The difference images were searched for companion candidates, and radially averaged contrast curves were computed. Though we achieve the lowest mass detection limits yet for angular separations ≥0.5 arcsec, no planetary mass companion to any of the seven white dwarfs, whose initial main-sequence masses were >2.8 M⊙, was found. Comparison with evolutionary models yields detection limits of ≈5–7 Jupiter masses (MJup) according to one model, and between 9 and ≈12 MJup according to another model, at physical separations corresponding to initial semimajor axis of ≥5–8 au (i.e. before the mass-loss events associated with the red and asymptotic giant branch phase of the host star). The study provides further evidence that initially dense cluster environments, which included O- and B-type stars, might not be highly conducive to the formation of massive circumstellar discs, and their transformation into giant planets (with m ≥ 6 MJup and a ≥6 au). This is in agreement with radial velocity surveys for exoplanets around G- and K-type giants, which did not find any planets around stars more massive than ≈3 M⊙.

CSABlock-based Cascade RCNN for Breast Mass Detection in Mammogram

2020 ◽  
Author(s):  
He Xiao ◽  
Qingfeng Wang ◽  
Zhiqin Liu ◽  
Jun Huang ◽  
Yuwei Zhou ◽  
...  
Keyword(s):  
Breast Mass ◽  
Mass Detection

scholarly journals Accuracy of New Deep Learning Model-Based Segmentation and Key-Point Multi-Detection Method for Ultrasonographic Developmental Dysplasia of the Hip (DDH) Screening

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1174
Author(s):  
Si-Wook Lee ◽  
Hee-Uk Ye ◽  
Kyung-Jae Lee ◽  
Woo-Young Jang ◽  
Jong-Ha Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intraclass Correlation ◽  
Alpha Angle ◽  
Learning Model ◽  
Developmental Dysplasia ◽  
Human Observer ◽  
Intraobserver Variability ◽  
Cad System ◽  
Deep Learning Model ◽  
Dysplasia Of The Hip

Hip joint ultrasonographic (US) imaging is the golden standard for developmental dysplasia of the hip (DDH) screening. However, the effectiveness of this technique is subject to interoperator and intraobserver variability. Thus, a multi-detection deep learning artificial intelligence (AI)-based computer-aided diagnosis (CAD) system was developed and evaluated. The deep learning model used a two-stage training process to segment the four key anatomical structures and extract their respective key points. In addition, the check angle of the ilium body balancing level was set to evaluate the system’s cognitive ability. Hence, only images with visible key anatomical points and a check angle within ±5° were used in the analysis. Of the original 921 images, 320 (34.7%) were deemed appropriate for screening by both the system and human observer. Moderate agreement (80.9%) was seen in the check angles of the appropriate group (Cohen’s κ = 0.525). Similarly, there was excellent agreement in the intraclass correlation coefficient (ICC) value between the measurers of the alpha angle (ICC = 0.764) and a good agreement in beta angle (ICC = 0.743). The developed system performed similarly to experienced medical experts; thus, it could further aid the effectiveness and speed of DDH diagnosis.

scholarly journals Development and validation of a liquid chromatography-tandem mass spectrometric method for the determination of lenalidomide in human plasma and its application on bioequivalence studies

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
R. Gopinath ◽  
S. T. Narenderan ◽  
M. Kumar ◽  
B. Babu
Keyword(s):  
Liquid Chromatography ◽  
Human Plasma ◽  
Internal Standard ◽  
Spectrometric Method ◽  
Tandem Mass ◽  
Mass Spectrometric Method ◽  
Mass Detection ◽  
Triple Quadrupole Mass Spectrometer ◽  
Liquid Chromatography Tandem Mass ◽  
Tandem Mass Spectrometric

AbstractA simple, sensitive, and specific liquid chromatography-tandem mass spectrophotometry (LC-MS/MS) method was developed and validated for the quantification of lenalidomide in human plasma. The separation was carried out on a symmetry, C18, 5-μm (50 × 4.6 mm) column as stationary phase and with an isocratic mobile phase of 0.1% formic acid in water-methanol in the ratio of (15:85, v/v) at a flow rate of 0.5 mL/min. Protonated ions formed by electrospray ionization in the positive mode were used to detect analyte and fluconazole (internal standard). The mass detection was made by monitoring the fragmentation of m/z 260.1/148.8 for lenalidomide and m/z 307.1/238.0 for internal standard on a triple quadrupole mass spectrometer. The developed method was validated over the concentration range of 10–1000 ng/mL for lenalidomide in human plasma with a correlation coefficient (r2) was 0.9930. The accuracy and precision values obtained from six different sets of quality control samples analyzed on separate occasions ranged from 99.41 to 106.97% and 2.88 to 4.22%, respectively. Mean extraction recoveries were 98.06% and 88.78% for the analyte and IS, respectively. The developed method was successfully applied for analyzing lenalidomide in human plasma samples.

scholarly journals A Novel Evaluation Method For Particle Deposition Measurement

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 927-934
Author(s):  
Tao Song ◽  
Chao Liu ◽  
Hengxuan Zhu ◽  
Min Zeng ◽  
Jin Wang
Keyword(s):  
Layer Thickness ◽  
Dielectric Layer ◽  
Gas Turbines ◽  
Particle Deposition ◽  
Optimization Design ◽  
Turbine Blades ◽  
Simulation Software ◽  
Circuit Board ◽  
Normal Operation ◽  
Mass Detection

Abstract Normal operation of gas turbines will be affected by deposition on turbine blades from particles mixed in fuels. This research shows that it is difficult to monitor the mass of the particles deposition on the wall surface in real time. With development of electronic technology, the antenna made of printed circuit board (PCB) has been widely used in many industrial fields. Microstrip antenna is first proposed for monitoring particles deposition to analyse the deposition law of the particles accumulated on the wall. The simulation software Computer Simulation Technology Microwave Studio (CST MWS) 2015 is used to conduct the optimization design of the PCB substrate antenna. It is found that the S11 of vivaldi antenna with arc gradient groove exhibits a monotonous increase with the increase of dielectric layer thickness, and this antenna is highly sensitive to the dielectric layer thickness. Moreover, a cold-state test is carried out by using atomized wax to simulate the deposition of pollutants. A relationship as a four number of times function is found between the capacitance and the deposited mass. These results provide an important reference for the mass detection of the particle deposition on the wall, and this method is suitable for other related engineering fields.

Sign in / Sign up

Export Citation Format

Share Document