A new method for the automatic retrieval of medical cases based on the RadLex ontology

Spanier, Assaf B.; Cohen, Dalia; Joskowicz, Leo

doi:10.1007/s11548-016-1496-y

Cited by 13 publications

(11 citation statements)

References 31 publications

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“…To better understand the results we will now provide a detailed analysis on the annotations for the ‘clinical finding’ and ‘anatomical entity’ subtrees of RadLex. These are two of the subtrees that probably would be more important when applying RadLex to a Information Retrieval system ( 21 ), a type of application for which the results of this study can be useful.…”

Section: Resultsmentioning

confidence: 99%

Impact of translation on named-entity recognition in radiology texts

Campos

Pedro²,

Couto

2017

Database

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Radiology reports describe the results of radiography procedures and have the potential of being a useful source of information which can bring benefits to health care systems around the world. One way to automatically extract information from the reports is by using Text Mining tools. The problem is that these tools are mostly developed for English and reports are usually written in the native language of the radiologist, which is not necessarily English. This creates an obstacle to the sharing of Radiology information between different communities. This work explores the solution of translating the reports to English before applying the Text Mining tools, probing the question of what translation approach should be used. We created MRRAD (Multilingual Radiology Research Articles Dataset), a parallel corpus of Portuguese research articles related to Radiology and a number of alternative translations (human, automatic and semi-automatic) to English. This is a novel corpus which can be used to move forward the research on this topic. Using MRRAD we studied which kind of automatic or semi-automatic translation approach is more effective on the Named-entity recognition task of finding RadLex terms in the English version of the articles. Considering the terms extracted from human translations as our gold standard, we calculated how similar to this standard were the terms extracted using other translations. We found that a completely automatic translation approach using Google leads to F-scores (between 0.861 and 0.868, depending on the extraction approach) similar to the ones obtained through a more expensive semi-automatic translation approach using Unbabel (between 0.862 and 0.870). To better understand the results we also performed a qualitative analysis of the type of errors found in the automatic and semi-automatic translations. Database URL: https://github.com/lasigeBioTM/MRRAD

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Section: Resultsmentioning

confidence: 99%

Impact of translation on named-entity recognition in radiology texts

Campos

Pedro²,

Couto

2017

Database

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“…JADUAL 1. Kajian yang telah dijalankan oleh ahli penyelidik Kajian Kaedah Modaliti Imej (Arakeri et al 2012) Pengelasan hierarki dan persamaan kandungan MRI (Suganya et al 2012) SVM digabungkan dengan maklumbalas relevan Ultrasound (Nakaram et al 2012) Discrete Wavelet Transform (DWT) X-Ray (Murala and Jonathan Wu 2013) Local ternary co-occurrence patterns MRI dan CT (Arthi et al 2013) CCM (Colour Co-occurrence Matrix) menggunakan peta warna Imej Perubatan Hue Saturation Value (HSV) (Grace et al 2014) Rangka Apache Hadoop Imej Perubatan Berasaskan kaedah graf menggunakan vertex set dan edge set PET-CT (Wan Ahmad et al 2014) Gabor transform, Discrete Wavelet Frame, Grey Level Histogram X-Ray dan kombinasi kaedah ini (Bergamasco and Nunes 2015) Fitur global dan tempatan: Distance Histogram Descriptor, Local Distance MRI Histogram Descriptor, dan 3D Hough Transform Descriptor (Kitanovski et al 2016) Berasaskan hasil pengkuantuman dan pengelasan SVM Imej Perubatan (Sparks and Madabhushi 2016) Out-of-Sample Extrapolation menggunakan Semi-Supervised Manifold Histologi Prostat Learning (OSE-SSL) (Nowaková et al 2017) Pengkuantuman vektor dengan fuzzy S-trees Mammogram (Spanier et al 2017) Hibrid: gabungan dengan penemuan radiologi dari laporan kes CT perubatan dan graf Radlex (Xu et al 2017 Dalam bidang perubatan, imej-imej kebiasaannya disimpan dalam format DICOM. Imej DICOM mengandungi pelbagai maklumat penting mengenai pesakit seperti identiti pesakit, jantina, modaliti imej, bahagian badan dan parameter mesin.…”

Section: Pangkalan Data Imejunclassified

Content-based Image Retrieval for Medical Application

Zaki¹,

Ahmad²,

Hussain³

et al. 2018

JKUKM

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ABSTRAK Dapatan semula imej (DSI) adalah sistem pencarian imej yang menggunakan ciri-ciri tertentu atau konteks khusus Kata kunci: Imej perubatan; sistem dapatan semula imej; aplikasi perubatan; modaliti x-ray; TBIRS ABSTRACT Image retrieval is an image searching system that uses certain characteristics or context in an image. In a medical field, image retrieval system has been used to provide the needed correct images to the physicians while the diagnosis and treatment process is being conducted. The earlier image retrieval system was a text-based image retrieval system (TBIRS) which using keyword for the image context and it requires human's help to manually make text annotation on the images. This system is still being widely used in the hospitals nowadays. The text annotation process is a laborious task especially when dealing with a huge database and is prone to human errors. To overcome the aforementioned issues, the approach of a content-based image retrieval system (CBIRS) with automatic indexing using visual features such as color

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“…[24252627] An example of a more specific diagnostic ontology is the well-known radiology ontology RadLex. [2829] To the best of our knowledge, there are no well-known and specific ontology in the histopathology area, although Quantitative Histopathology Image Ontology (QHIO) is under development. QHIO is an ontology covering terms representing the different types and subtypes of histopathological images, imaging processes and techniques, and computational algorithms.…”

Section: Introductionmentioning

confidence: 99%

Annotations, Ontologies, and Whole Slide Images – Development of an Annotated Ontology-Driven Whole Slide Image Library of Normal and Abnormal Human Tissue

Lindman¹,

Rose

Lindvall

et al. 2019

Journal of Pathology Informatics

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Objective: Digital pathology is today a widely used technology, and the digitalization of microscopic slides into whole slide images (WSIs) allows the use of machine learning algorithms as a tool in the diagnostic process. In recent years, “deep learning” algorithms for image analysis have been applied to digital pathology with great success. The training of these algorithms requires a large volume of high-quality images and image annotations. These large image collections are a potent source of information, and to use and share the information, standardization of the content through a consistent terminology is essential. The aim of this project was to develop a pilot dataset of exhaustive annotated WSI of normal and abnormal human tissue and link the annotations to appropriate ontological information. Materials and Methods: Several biomedical ontologies and controlled vocabularies were investigated with the aim of selecting the most suitable ontology for this project. The selection criteria required an ontology that covered anatomical locations, histological subcompartments, histopathologic diagnoses, histopathologic terms, and generic terms such as normal, abnormal, and artifact. WSIs of normal and abnormal tissue from 50 colon resections and 69 skin excisions, diagnosed 2015-2016 at the Department of Clinical Pathology in Linköping, were randomly collected. These images were manually and exhaustively annotated at the level of major subcompartments, including normal or abnormal findings and artifacts. Results: Systemized nomenclature of medicine clinical terms (SNOMED CT) was chosen, and the annotations were linked to its codes and terms. Two hundred WSI were collected and annotated, resulting in 17,497 annotations, covering a total area of 302.19 cm 2 , equivalent to 107,7 gigapixels. Ninety-five unique SNOMED CT codes were used. The time taken to annotate a WSI varied from 45 s to over 360 min, a total time of approximately 360 h. Conclusion: This work resulted in a dataset of 200 exhaustive annotated WSIs of normal and abnormal tissue from the colon and skin, and it has informed plans to build a comprehensive library of annotated WSIs. SNOMED CT was found to be the best ontology for annotation labeling. This project also demonstrates the need for future development of annotation tools in order to make the annotation process more efficient.

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A new method for the automatic retrieval of medical cases based on the RadLex ontology

Abstract: The increasing amount of medical imaging data acquired in clinical practice constitutes a vast database of untapped diagnostically relevant information. This paper presents a new hybrid approach to retrieving the most relevant medical cases based on textual and image information.

Cited by 13 publications

References 31 publications

Impact of translation on named-entity recognition in radiology texts

Impact of translation on named-entity recognition in radiology texts

Content-based Image Retrieval for Medical Application

Annotations, Ontologies, and Whole Slide Images – Development of an Annotated Ontology-Driven Whole Slide Image Library of Normal and Abnormal Human Tissue

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