scholarly journals Benign solitary breast masses in the prevalent screening round: do they contribute to a high recall rate?

2010 ◽  
Vol 12 (S3) ◽  
Author(s):  
N Hoskins ◽  
L Edwards ◽  
K Gower-Thomas ◽  
V Shah
Keyword(s):  
Recall Rate ◽  
Breast Masses ◽  
High Recall ◽  
High Recall Rate ◽  
Prevalent Screening

scholarly journals Comparison of Mammography and Ultrasonography for Early Detection of Breast Cancer

2021 ◽  
Vol 15 (7) ◽  
pp. 1450-1455
Author(s):  
Samina Mahmood ◽  
M Nawaz Anjum ◽  
Faiza Farooq ◽  
S.Amir Gilani ◽  
Mehreen Fatima ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Early Detection ◽  
Meta Analysis ◽  
Recall Rate ◽  
Screening Methods ◽  
Major Purpose ◽  
High Recall ◽  
Dense Breasts ◽  
High Recall Rate

Aim: This systematic review is specifically aimed to compare mammography and ultrasonography in early detection of breast cancer. For this systematic review, major purpose is to compare both screening methods and also analyze the performance of supplemental ultrasonography for early detection of breast cancer. Methodology: For this systematic review, total 23 studies are included which follow the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Electronic articles from year 2007 to from year 2017 on PUB Med, online Willey library, and Science Direct site were searched by using keywords related to sonographic and mammography imaging for breast cancer. Results: Out of 23 studies, 12 studies are conducted on women with dense breasts. Twenty studies performed their imaging with hand held ultrasound (HHUS). Out of twenty-three studies, sixteen studies followed BI-RADS procedures. In eleven studies that used joint methods, it was observed that mammography (MAM) has 65% whereas ultrasound (US) has 68% efficiency for early detection of breast cancer. 88% area under a cover (AUCs) among MAM and 98% among US imaging was observed. No major difference was found in sensitivity and specificity of both techniques. Conclusion: Study concludes that Ultrasound is more efficient to diagnose factors suggestive of breast cancer that cannot be detected on mammography. It also has the potential to evaluate cancer among dense breast women but unfortunately in some cases, it may cause a high recall rate. Keywords: Breast, Cancer, Mammography, Ultrasonography, Screening.

Chord-Based Indexing Model to Support Complex Query and Load Balancing

2011 ◽  
Vol 474-476 ◽  
pp. 1781-1786
Author(s):  
Xiu Mei Zhao ◽  
Fang Ai Liu ◽  
Song Qin
Keyword(s):  
Load Balancing ◽  
Recall Rate ◽  
High Recall ◽  
Complex Query ◽  
New Model ◽  
File Storage ◽  
Fuzzy Matching ◽  
High Recall Rate ◽  
Indexing Model

This paper presents a new model (SMF-Chord) which is expanded from Chord. SMF-Chord uses double-fingerprint, double-mapping and dynamic forwarding mechanism to support multi-attribute multi-keyword fuzzy-matching query, and also has a load balancing mechanism which includes three parts: similar-close transposition, forward balancing and hot-set cache. The experiment results show SMF-Chord has high recall rate with low storage redundancy, and it can effectively balance load when node mapping load, file storage load, or query load is unbalanced.

Early outcome of mammography screening in Copenhagen 1991–99

2002 ◽  
Vol 9 (3) ◽  
pp. 115-119 ◽  
Author(s):  
I. Vejborg ◽  
A.H. Olsen ◽  
M-B. Jensen ◽  
F. Rank ◽  
U.B. Tange ◽  
...  
Keyword(s):  
False Positive ◽  
Mammography Screening ◽  
Detection Rate ◽  
Screening Programme ◽  
Recall Rate ◽  
Opportunistic Screening ◽  
Breast Cancers ◽  
High Recall ◽  
Early Outcome ◽  
High Recall Rate

OBJECTIVES: To evaluate the early outcome of an organised mammography screening programme in an area with little opportunistic screening. SETTING: The municipality of Copenhagen, Denmark, during four invitation rounds 1991–99. METHODS: The following outcome measures were used: rates of participation, recall, false positive, and cancer detection. Benign biopsy, distribution of tumour size, lymph node status, and malignancy grade. RESULTS: A total of 106 933 screens were undertaken, and 824 invasive breast carcinomas or CIS were detected. The detection rate was 11.9 per 1000 participants in the first invitation round, and it continued to be high in subsequent rounds. The percentage of CIS cases was 11%. Coverage declined from 71% in the first round to 62% in the fourth, although 91% of those participating in the previous three rounds attended. The programme operated with a high recall rate. The false positive rate was also high, being 5.6% at first screen, and 1.8% later on. However, 90% of false positives were sorted out already at assessment. The percentage of screen detected invasive breast cancers with a tumour diameter ≤10 mm was 39% compared with 16% of all invasive breast cancers in these age groups in Copenhagen before screening. CONCLUSION: Copenhagen is an area with a high incidence of breast cancer and with relatively little opportunistic screening. The start of a screening programme with a high recall rate in this area resulted in a detection rate above 1%. The Copenhagen programme met or exceeded most of the interim measures recommended in the European Guidelines.

Hierarchical Text Classification Based on LDA and Domain Ontology

2013 ◽  
Vol 411-414 ◽  
pp. 1112-1116 ◽  
Author(s):  
Wei An ◽  
Qi Hua Liu
Keyword(s):  
Text Classification ◽  
Recall Rate ◽  
Domain Ontology ◽  
Experimental Results ◽  
New Method ◽  
Accuracy Rate ◽  
High Recall ◽  
High Recall Rate ◽  
Hierarchical Text Classification

This paper combines domain ontology and LDA model to propose a new method of hierarchical web text classification. Experimental results show that the method has good performance with high recall rate and accuracy rate.

scholarly journals Exploring high reliable substructures in auto-reconstructions of a neuron

2021 ◽  
Author(s):  
Yishan He ◽  
Jiajin Huang ◽  
Gaowei Wu ◽  
Jian Yang
Keyword(s):  
State Of The Art ◽  
Recall Rate ◽  
Local Alignment ◽  
Alignment Algorithm ◽  
Neuron Tracing ◽  
Digital Reconstruction ◽  
High Recall Rate ◽  
Multiple Species ◽  
Multiple Reference ◽  
Precision Rate

Abstract The digital reconstruction of a neuron is the most direct and effective way to investigate its morphology. Many automatic neuron tracing methods have been proposed, but without manual check it is difficult to know whether a reconstruction or which substructure in a reconstruction is accurate. For a neuron’s reconstructions generated by multiple automatic tracing methods with different principles or models, their common substructures are highly reliable and named individual motifs. In this work, we propose a Vaa3D based method called Lamotif to explore individual motifs in automatic reconstructions of a neuron. Lamotif utilizes the local alignment algorithm in BlastNeuron to extract local alignment pairs between a specified objective reconstruction and multiple reference reconstructions, and combines these pairs to generate individual motifs on the objective reconstruction. The proposed Lamotif is evaluated on reconstructions of 163 multiple species neurons, which are generated by four state-of-the-art tracing methods. Experimental results show that individual motifs are almost on corresponding gold standard reconstructions and have much higher precision rate than objective reconstructions themselves. Furthermore, an objective reconstruction is mostly quite accurate if its individual motifs have high recall rate. Individual motifs contain common geometry substructures in multiple reconstructions, and can be used to select some accurate substructures from a reconstruction or some accurate reconstructions from automatic reconstruction dataset of different neurons.

scholarly journals SOFIA: A Novel Automated Breast Ultrasound System Used on Patients in the Prone Position: A Pilot Study on Lesion Detection in Comparison to Handheld Grayscale Ultrasound

2018 ◽  
Vol 78 (05) ◽  
pp. 499-505 ◽  
Author(s):  
André Farrokh ◽  
Harika Erdönmez ◽  
Fritz Schäfer ◽  
Nicolai Maass
Keyword(s):  
Prone Position ◽  
Recall Rate ◽  
Breast Ultrasound ◽  
Lesion Detection ◽  
The Novel ◽  
Scanning Time ◽  
High Recall Rate ◽  
Study Population ◽  
Sensitivity Specificity ◽  
Automated Breast Ultrasound

Abstract Introduction Most of the currently available automated breast ultrasound systems require patients to be in the supine position. Previous data, however, show a high recall rate with this method due to artifacts. The novel automated breast ultrasound scanner SOFIA scans the breast with the patient in a prone position, resulting in even compression of breast tissue. We present our initial results with this examination method. Material and Methods 63 patients were analyzed using a handheld B-mode ultrasound. In cases of BI-RADS 1, 2 or 5, a SOFIA scan was performed. Sensitivity, specificity and accuracy were calculated. Interobserver agreement was evaluated using Cohenʼs kappa. The duration of the scan was measured for both methods. Results No BI-RADS 5 lesion was missed with SOFIA. The SOFIA had an additional recall rate of 16.67% compared to B-mode ultrasound. The sensitivity, specificity and accuracy of SOFIA was 100, 83.33 and 88.89%, respectively. Cohenʼs kappa showed substantial agreement (κ = 0.769) between examiner 1 (B-mode) and examiner 2 (SOFIA). The mean scan duration for the B-mode system and the SOFIA system was 24.21 minutes and 12.94 minutes, respectively. In four cases, D-cup breasts were not scanned in their entirety. Conclusion No cancer was missed when SOFIA was used in this preselected study population. The scanning time was approximately half of that required for B-mode ultrasound. The additional unnecessary recall rate was 16.67%. Larger D cup-size breasts were difficult to position and resulted in an incomplete image in four cases.

scholarly journals A Data Mining Method for Students' Behavior Understanding

2020 ◽  
Vol 15 (06) ◽  
pp. 18
Author(s):  
Wei Na
Keyword(s):  
Student Behavior ◽  
Behavioral Model ◽  
Recall Rate ◽  
Learning Performance ◽  
Multiple Perspectives ◽  
Additional Information ◽  
Behavior Understanding ◽  
Learning Path ◽  
High Recall Rate ◽  
Collaborative Filtering Algorithm

To model students' behavior and describe their behavior characteristics accurately and comprehensively, a framework for predicting students' learning performance based on behavioral model is proposed, which extracts features from multiple perspectives to describe behaviors more comprehensively, including statistical features and association features. In addition, a multi-task model is designed for fine-grained prediction of students' learning performance in the curriculum. A framework for predicting mastery based on online learning behavior is also put forward. Additional context information is added to the collaborative filtering algorithm, including student-knowledge-point mastery and class-knowledge-point, and students' mastery is predicted according to the learning path excavated. Considering the time-varying of mastery, the approximate curve of students' mastery of knowledge points is fitted according to the Ebinhaus forgetting curve. The experiments show that the proposed framework has a high recall rate for the prediction of learning performance, and also shows a certain practicability for early warning. Further, based on the model, the correlation between student behavior patterns and learning performance is discussed. The addition of additional information has improved the prediction efficiency, especially the operational efficiency. At the same time, the proposed framework can not only dynamically assess students' master of knowledge, but also facilitate the system to review feedback or adjust the learning order, and provide personalized learning services.

scholarly journals A Shallow–Deep Feature Fusion Method for Pedestrian Detection

2021 ◽  
Vol 11 (19) ◽  
pp. 9202
Author(s):  
Daxue Liu ◽  
Kai Zang ◽  
Jifeng Shen
Keyword(s):  
Detection Method ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Recall Rate ◽  
Fusion Method ◽  
Discriminative Power ◽  
Deep Feature ◽  
Proposal A ◽  
High Recall Rate ◽  
Feature Based

In this paper, a shallow–deep feature fusion (SDFF) method is developed for pedestrian detection. Firstly, we propose a shallow feature-based method under the ACF framework of pedestrian detection. More precisely, improved Haar-like templates with Local FDA learning are used to filter the channel maps of ACF such that these Haar-like features are able to improve the discriminative power and therefore enhance the detection performance. The proposed shallow feature is also referred to as weighted subset-haar-like feature. It is efficient in pedestrian detection with a high recall rate and precise localization. Secondly, the proposed shallow feature-based detection method operates as a region proposal. A classifier equipped with ResNet is then used to refine the region proposals to judge whether each region contains a pedestrian or not. The extensive experiments evaluated on INRIA, Caltech, and TUD-Brussel datasets show that SDFF is an effective and efficient method for pedestrian detection.

scholarly journals PurExt: Automated Extraction of the Purpose-Aware Rule from the Natural Language Privacy Policy in IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lu Yang ◽  
Xingshu Chen ◽  
Yonggang Luo ◽  
Xiao Lan ◽  
Li Chen
Keyword(s):  
Data Collection ◽  
Natural Language ◽  
Recall Rate ◽  
Semantic Role Labeling ◽  
Privacy Policies ◽  
Novel Approach ◽  
High Recall Rate ◽  
Iot Devices ◽  
Machine Readable ◽  
The Internet Of Things

The extensive data collection performed by the Internet of Things (IoT) devices can put users at risk of data leakage. Consequently, IoT vendors are legally obliged to provide privacy policies to declare the scope and purpose of the data collection. However, complex and lengthy privacy policies are unfriendly to users, and the lack of a machine-readable format makes it difficult to check policy compliance automatically. To solve these problems, we first put forward a purpose-aware rule to formalize the purpose-driven data collection or use statement. Then, a novel approach to identify the rule from natural language privacy policies is proposed. To address the issue of diversity of purpose expression, we present the concepts of explicit and implicit purpose, which enable using the syntactic and semantic analyses to extract purposes in different sentences. Finally, the domain adaption method is applied to the semantic role labeling (SRL) model to improve the efficiency of purpose extraction. The experiments that are conducted on the manually annotated dataset demonstrate that this approach can extract purpose-aware rules from the privacy policies with a high recall rate of 91%. The implicit purpose extraction of the adapted model significantly improves the F1-score by 11%.

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