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Ontology Knowledge Mining Based Association Rules Ranking
2016
Procedia Computer Science
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In this paper, we design a distinct approach for ranking semantically interesting association rules involving the use of an ontology knowledge mining approach. ...
Medical association rules induction is used to discover useful correlations between pertinent concepts from large medical databases. ...
For example, in the mammographic domain, the mammographic ontology might be used to describe: the radiological observations associated with feature descriptors, mammogram Bi-Rads classification, clinical ...
doi:10.1016/j.procs.2016.08.147
fatcat:u6oi2aw7rnendmbcidf2clsjtm
Automatic abstraction of imaging observations with their characteristics from mammography reports
2014
JAMIA Journal of the American Medical Informatics Association
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extraction needed for data mining and decision support. ...
In order to evaluate our system, we selected 300 mammography reports from a hospital report database. ...
(optional)
Table 2 2 Sample rules for linking modifiers with Imaging Observation and Location entities Rules
Example sentences
Features of the entities
Rule 1: Any modifier inside the text of an ...
doi:10.1136/amiajnl-2014-003009
pmid:25352567
fatcat:prgwpvyfqrbazjfhr2mxoj444m
The implementation of natural language processing to extract index lesions from breast magnetic resonance imaging reports
2019
BMC Medical Informatics and Decision Making
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Natural language processing (NLP) has been used for information extraction from mammography reports. However, few studies have investigated NLP in breast MRI data based on free-form text. ...
Second, the index lesion was defined as the lesion with the largest number of imaging features. Third, we extracted the values of each imaging feature and the BI-RADS category from each index lesion. ...
Acknowledgements The authors would like to thank Changzheng He for providing guidance for the implementation of the natural language processing software tool. ...
doi:10.1186/s12911-019-0997-3
pmid:31888615
pmcid:PMC6937920
fatcat:wrqwix2v4zen5i344y2m2ckvvu
Microscopic Tumour Classification by Digital Mammography
2021
Journal of Healthcare Engineering
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optimize the network training to achieve a fine segmentation of the lesion area, and demonstrate the accuracy and feasibility of the two models in medical image segmentation. ...
In this paper, we investigate the classification of microscopic tumours using full digital mammography images. ...
Experimental Design Analysis
Experimental Design. 8872 images of 2218 pathologically confirmed mammographic cases were annotated and classified by two radiologists using manual annotation software based ...
doi:10.1155/2021/6635947
pmid:33613927
pmcid:PMC7878100
fatcat:2wvyutuacbcx7gg53srefddpam
A Brief Survey on Breast Cancer Diagnostic with Deep Learning Schemes Using Multi-Image Modalities
2020
IEEE Access
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This research focusses on providing benefits and risks of breast multi-imaging modalities, segmentation schemes, feature extraction, classification of breast abnormalities through state-of-the-art deep ...
The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due to technical issues in imaging qualities and heterogeneous breast densities which increases the false ...
DEEP LEARNING IN IMAGE FEATURE EXTRACTION Feature extraction is a classification step used for feature calculation of ROI with associated properties such as size, shape, homogeneity, and tissue density ...
doi:10.1109/access.2020.3021343
fatcat:czvctyngmjg6bhzinpmrfmht64
Case Retrieval in Medical Databases by Fusing Heterogeneous Information
2011
IEEE Transactions on Medical Imaging
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Once the available images in a query document are characterized, a degree of match, between the query document and each reference document stored in the database, is defined for each attribute (an image ...
It was designed to retrieve possibly incomplete documents, consisting of several images and semantic information, from a database; more complex data types such as videos can also be included in the framework ...
Medical documents often consist of digital information such as images and symbolic information such as clinical annotations. ...
doi:10.1109/tmi.2010.2063711
pmid:20693107
fatcat:rh2g5qnioffj7edamcvgp2o5um
Deep learning in medical imaging and radiation therapy
2018
Medical Physics (Lancaster)
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We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods ...
The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies ...
Here, radiomic features of tumors are used as image-based phenotypes for correlative and association analysis with genomics as well as histopathology. ] [222] [223] [224] Use of DL methods as feature ...
doi:10.1002/mp.13264
pmid:30367497
fatcat:bottst5mvrbkfedbuocbrstcnm
Breast Imaging in the Era of Big Data: Structured Reporting and Data Mining
2016
American Journal of Roentgenology
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From 130 rules, two were selected as possible predictors of malignancy. ...
For example, data mining to identify drug reactions can include, in addition to the medical literature, online self-reporting and electronic medical records [59] . ...
doi:10.2214/ajr.15.15396
pmid:26587797
pmcid:PMC4876713
fatcat:zlityyz5rvernjc4el2246m2cq
Intelligent Image Retrieval Techniques: A Survey
2014
Journal of Applied Research and Technology
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While working with digital images, quite often it is necessary to search for a specific image for a particular situation based on the visual contents of the image. ...
In this research, the aim is to highlight the efforts of researchers who conducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques. ...
Acknowledgements We are grateful to Department of Computer Science, COMSATS Institute of Information Technology Pakistan for providing us a platform to conduct this research study. ...
doi:10.1016/s1665-6423(14)71609-8
fatcat:ce3qthtoe5bdthys3yf6vadkvy
Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology
2018
Journal of Healthcare Engineering
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Inspired by gestalt psychology, we combine human cognitive characteristics with knowledge of radiologists in medical image analysis. ...
The experiments are performed on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) data sets. ...
ROIs are obtained by clustering these visual patches based on texture features F. e features of each ROI are represented by the average value of associated patches as shown in (8) , where M j is the total ...
doi:10.1155/2018/4015613
pmid:29854359
pmcid:PMC5954872
fatcat:i5zrvdpdhvbdjche7b32zpprki
Cross-Sectional Relatedness Between Sentences in Breast Radiology Reports: Development of an SVM Classifier and Evaluation Against Annotations of Five Breast Radiologists
2013
Journal of digital imaging
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Thirteen numerical features are developed to characterize pairs of sentences; the optimal feature set is sought through forward selection. Inter-rater agreement is F-measure 0.623. ...
Report length does not correlate with the classifier's performance (correlation coefficient=−0.073). SVM classifier has average F-measure of 0.505 against annotations by breast radiologists. ...
Acknowledgments The authors gratefully acknowledge Yassine Benajiba, Steffen Pauws, and the anonymous referees for valuable comments on an earlier version of this paper. ...
doi:10.1007/s10278-013-9612-9
pmid:23817629
pmcid:PMC3782592
fatcat:67zcyya3onhjzizmjrtenyyuuq
A Data Mining-Based OLAP Aggregation of Complex Data
2006
International Journal of Data Warehousing and Mining
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In this paper, we associate OLAP and data mining to cope advanced analysis on complex data. We provide a generalized OLAP operator, called OpAC, based on the AHC. ...
A Data Mining-Based OLAP Aggregation 4 measures. For example, a user wants to observe the sum of sales amount of products according to years and regions. ...
Tjioe and Taniar (2005) propose a method for mining association rules in data warehouses. ...
doi:10.4018/jdwm.2006100101
fatcat:ok34fyhiknfgldkzujes7w45dm
From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities
[article]
2019
arXiv
pre-print
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Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals and the availability of extensive set of medical images through ...
models, and is expected to become a critical component for integration of image-derived information for personalized treatment in the near future. ...
This strategy is adopted in Reference [42] , where a pre-trained CNN is used for breast cancer classification based on mammographic images. ...
arXiv:1808.07954v3
fatcat:huc23wcklfey5aetnlbe6o4h34
Deep Learning for Health Informatics
2017
IEEE journal of biomedical and health informatics
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The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health. ...
automatically optimized high-level features and semantic interpretation from the input data. ...
Deep Learning for Medical Imaging Automatic medical imaging analysis is crucial to modern medicine. Diagnosis based on the interpretation of images can be highly subjective. ...
doi:10.1109/jbhi.2016.2636665
pmid:28055930
fatcat:24hfhfasljhehb2phndoyu5rnm
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine
2020
Database: The Journal of Biological Databases and Curation
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the way for a new data-centric era of discovery in healthcare. ...
Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions ...
Christopher Bonin for providing editorial support. ...
doi:10.1093/database/baaa010
pmid:32185396
pmcid:PMC7078068
fatcat:ypsuz5dewvcgtpjx4vjkhi545q
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