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Ontology Knowledge Mining Based Association Rules Ranking

Rihab Idoudi, Karim Saheb Ettabaa, Basel Solaiman, Kamel Hamrouni
2016 Procedia Computer Science  
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

S. Bozkurt, J. A. Lipson, U. Senol, D. L. Rubin, H. Bulu
2014 JAMIA Journal of the American Medical Informatics Association  
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

Yi Liu, Qing Liu, Chao Han, Xiaodong Zhang, Xiaoying Wang
2019 BMC Medical Informatics and Decision Making  
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

Jingjing Yang, Huichao Li, Ning Shi, Qifan Zhang, Yanan Liu, Zhihan Lv
2021 Journal of Healthcare Engineering  
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

Tariq Mahmood, Jianqiang Li, Yan Pei, Faheem Akhtar, Azhar Imran, Khalil ur Rehman
2020 IEEE Access  
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

Gwénolé Quellec, Mathieu Lamard, Guy Cazuguel, Christian Roux, Béatrice Cochener
2011 IEEE Transactions on Medical Imaging  
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

Berkman Sahiner, Aria Pezeshk, Lubomir M. Hadjiiski, Xiaosong Wang, Karen Drukker, Kenny H. Cha, Ronald M. Summers, Maryellen L. Giger
2018 Medical Physics (Lancaster)  
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

Laurie R. Margolies, Gaurav Pandey, Eliot R. Horowitz, David S. Mendelson
2016 American Journal of Roentgenology  
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

Mussarat Yasmin, Sajjad Mohsin, Muhammad Sharif
2014 Journal of Applied Research and Technology  
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

Hongyu Wang, Jun Feng, Qirong Bu, Feihong Liu, Min Zhang, Yu Ren, Yi Lv
2018 Journal of Healthcare Engineering  
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

Merlijn Sevenster, Yuechen Qian, Hiroyuki Abe, Johannes Buurman
2013 Journal of digital imaging  
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

Riadh Ben Messaoud, Omar Boussaid, Sabine Loudcher Rabaséda
2006 International Journal of Data Warehousing and Mining  
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]

Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis, Anastasia Oikonomou, Habib Benali
2019 arXiv   pre-print
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

Daniele Ravi, Charence Wong, Fani Deligianni, Melissa Berthelot, Javier Andreu-Perez, Benny Lo, Guang-Zhong Yang
2017 IEEE journal of biomedical and health informatics  
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  
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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