88 research outputs found
PadChest: A large chest x-ray image dataset with multi-label annotated reports
We present a labeled large-scale, high resolution chest x-ray dataset for the
automated exploration of medical images along with their associated reports.
This dataset includes more than 160,000 images obtained from 67,000 patients
that were interpreted and reported by radiologists at Hospital San Juan
Hospital (Spain) from 2009 to 2017, covering six different position views and
additional information on image acquisition and patient demography. The reports
were labeled with 174 different radiographic findings, 19 differential
diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and
mapped onto standard Unified Medical Language System (UMLS) terminology. Of
these reports, 27% were manually annotated by trained physicians and the
remaining set was labeled using a supervised method based on a recurrent neural
network with attention mechanisms. The labels generated were then validated in
an independent test set achieving a 0.93 Micro-F1 score. To the best of our
knowledge, this is one of the largest public chest x-ray database suitable for
training supervised models concerning radiographs, and the first to contain
radiographic reports in Spanish. The PadChest dataset can be downloaded from
http://bimcv.cipf.es/bimcv-projects/padchest/
Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports knowledge representation
Background: The use of knowledge models facilitates information retrieval, knowledge base development, and therefore supports new knowledge discovery that ultimately enables decision support applications. Most existing works have employed machine learning techniques to construct a knowledge base. However, they often suffer from low precision in extracting entity and relationships. In this paper, we described a data-driven sublanguage pattern mining method that can be used to create a knowledge model. We combined natural language processing (NLP) and semantic network analysis in our model generation pipeline.
Methods: As a use case of our pipeline, we utilized data from an open source imaging case repository, Radiopaedia.org, to generate a knowledge model that represents the contents of medical imaging reports. We extracted entities and relationships using the Stanford part-of-speech parser and the “Subject:Relationship:Object” syntactic data schema. The identified noun phrases were tagged with the Unified Medical Language System (UMLS) semantic types. An evaluation was done on a dataset comprised of 83 image notes from four data sources.
Results: A semantic type network was built based on the co-occurrence of 135 UMLS semantic types in 23,410 medical image reports. By regrouping the semantic types and generalizing the semantic network, we created a knowledge model that contains 14 semantic categories. Our knowledge model was able to cover 98% of the content in the evaluation corpus and revealed 97% of the relationships. Machine annotation achieved a precision of 87%, recall of 79%, and F-score of 82%.
Conclusion: The results indicated that our pipeline was able to produce a comprehensive content-based knowledge model that could represent context from various sources in the same domain
Utilisation des ontologies dans le contexte de l'Imagerie par Résonance Magnétique
L imagerie médicale et en particulier l Imagerie par Résonance Magnétique (IRM) occupe une place de choix dans les décisions médicales. Malgré des techniques et des pratiques d examens comparables les industriels du secteur utilisent un vocabulaire différent pour décrire les événements de l expérience IRM. Les ontologies permettent de résoudre cette problématique. En les intégrant dans un système informatique nous avons choisi de proposer des solutions innovantes pour trois situations quotidiennes: l annotation d examen, la reconnaissance et la correction d artéfact et l aide à la prescription d examen. Les connaissances du domaine IRM sont issues de la littérature et de la pratique quotidienne. DICOM, élément incontournable à l échange de données en imagerie médicale, a été le point de départ de l élaboration de l ontologie. Les connaissances sur les artéfacts en IRM sont en partie issues d une collaboration avec l université de Texas A&M Temple. Pour l aide à la prescription d examen nous avons choisi une situation clinique représentative la demande d IRM dans le cadre des traumatismes du genou. Les statistiques proviennent d une étude réalisée dans le service de médecine du sport du CHU de Rennes. Les systèmes élaborés permettent aux utilisateurs d utiliser les ontologies sans y être confronté et permettent l analyse de l entête DICOM d une image, l annotation de l image, la comparaison d une image IRM pour la correction d artéfact et l aide à la prescription d IRM est sous la forme d un serveur web permettant à l utilisateur de connaître en fonction des signes cliniques présents, la probabilité d avoir une lésion lors de la réalisation de l IRM. Nous avons démontré la possibilité d utilisation des ontologies pour améliorer l exercice quotidien des praticiens. Les techniques utilisées montre la possibilité d utiliser les ontologies en les associant aux images et aux probabilités. Le système choisi, interface permettra l évolution vers une technologie de type webservice.Magnetic resonance imaging (MRI) is a key examination in medical decision making. Despite MRI technics are slightly similar, each industrial has developed his own vocabulary to describe the MRI experience. Ontologies have been developed to help in such situations. We have decided to create IT solutions using ontology for three daily radiological situations: exams annotation, MRI artifacts recognition and correction and exam appropriateness. The domain knowledge is extracted from literature and everyday practice. DICOM, as key element for data exchange in radiology, has been used to create the ontology. Concerning MRI artifacts, a part of the knowledge comes from a collaborative work with the university of Texas A&M Temple. Concerning exam appropriateness, we have chosen a representative clinical situation: interest of knee MRI in case of knee trauma. The statistical data are coming from a clinical study done in the CHU of Rennes. Our systems allow users to take benefits of ontology without facing it. They give a DICOM header analysis, proposed an image annotation, compare image to correct MRI artifacts and help physicians to judge MRI appropriateness in case of knee trauma. We have demonstrated that ontologies could be used to improve daily practice in radiology and that ontologies could be associated to image and statistical data. Future of this work could be a system transformation into a web service.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF
Biomedical informatics and translational medicine
Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams
Recuperação de informação multimodal em repositórios de imagem médica
The proliferation of digital medical imaging modalities in hospitals and other
diagnostic facilities has created huge repositories of valuable data, often
not fully explored. Moreover, the past few years show a growing trend
of data production. As such, studying new ways to index, process and
retrieve medical images becomes an important subject to be addressed by
the wider community of radiologists, scientists and engineers. Content-based
image retrieval, which encompasses various methods, can exploit the visual
information of a medical imaging archive, and is known to be beneficial to
practitioners and researchers. However, the integration of the latest systems
for medical image retrieval into clinical workflows is still rare, and their
effectiveness still show room for improvement.
This thesis proposes solutions and methods for multimodal information
retrieval, in the context of medical imaging repositories. The major
contributions are a search engine for medical imaging studies supporting
multimodal queries in an extensible archive; a framework for automated
labeling of medical images for content discovery; and an assessment and
proposal of feature learning techniques for concept detection from medical
images, exhibiting greater potential than feature extraction algorithms that
were pertinently used in similar tasks. These contributions, each in their
own dimension, seek to narrow the scientific and technical gap towards
the development and adoption of novel multimodal medical image retrieval
systems, to ultimately become part of the workflows of medical practitioners,
teachers, and researchers in healthcare.A proliferação de modalidades de imagem médica digital, em hospitais,
clĂnicas e outros centros de diagnĂłstico, levou Ă criação de enormes
repositĂłrios de dados, frequentemente nĂŁo explorados na sua totalidade.
Além disso, os últimos anos revelam, claramente, uma tendência para o
crescimento da produção de dados. Portanto, torna-se importante estudar
novas maneiras de indexar, processar e recuperar imagens médicas, por
parte da comunidade alargada de radiologistas, cientistas e engenheiros. A
recuperação de imagens baseada em conteúdo, que envolve uma grande
variedade de métodos, permite a exploração da informação visual num
arquivo de imagem mĂ©dica, o que traz benefĂcios para os mĂ©dicos e
investigadores. Contudo, a integração destas soluções nos fluxos de trabalho
é ainda rara e a eficácia dos mais recentes sistemas de recuperação de
imagem médica pode ser melhorada.
A presente tese propõe soluções e métodos para recuperação de informação
multimodal, no contexto de repositórios de imagem médica. As contribuições
principais sĂŁo as seguintes: um motor de pesquisa para estudos de imagem
mĂ©dica com suporte a pesquisas multimodais num arquivo extensĂvel; uma
estrutura para a anotação automática de imagens; e uma avaliação e
proposta de técnicas de representation learning para deteção automática de
conceitos em imagens médicas, exibindo maior potencial do que as técnicas
de extração de features visuais outrora pertinentes em tarefas semelhantes.
Estas contribuições procuram reduzir as dificuldades tĂ©cnicas e cientĂficas
para o desenvolvimento e adoção de sistemas modernos de recuperação de
imagem médica multimodal, de modo a que estes façam finalmente parte
das ferramentas tĂpicas dos profissionais, professores e investigadores da área
da saúde.Programa Doutoral em Informátic
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