1,701 research outputs found
ALEC: Active learning with ensemble of classifiers for clinical diagnosis of coronary artery disease
Invasive angiography is the reference standard for coronary artery disease (CAD) diagnosis but is expensive and
associated with certain risks. Machine learning (ML) using clinical and noninvasive imaging parameters can be
used for CAD diagnosis to avoid the side effects and cost of angiography. However, ML methods require labeled
samples for efficient training. The labeled data scarcity and high labeling costs can be mitigated by active
learning. This is achieved through selective query of challenging samples for labeling. To the best of our
knowledge, active learning has not been used for CAD diagnosis yet. An Active Learning with Ensemble of
Classifiers (ALEC) method is proposed for CAD diagnosis, consisting of four classifiers. Three of these classifiers
determine whether a patientâs three main coronary arteries are stenotic or not. The fourth classifier predicts
whether the patient has CAD or not. ALEC is first trained using labeled samples. For each unlabeled sample, if the
outputs of the classifiers are consistent, the sample along with its predicted label is added to the pool of labeled
samples. Inconsistent samples are manually labeled by medical experts before being added to the pool. The
training is performed once more using the samples labeled so far. The interleaved phases of labeling and training
are repeated until all samples are labeled. Compared with 19 other active learning algorithms, ALEC combined
with a support vector machine classifier attained superior performance with 97.01% accuracy. Our method is
justified mathematically as well. We also comprehensively analyze the CAD dataset used in this paper. As part of
dataset analysis, features pairwise correlation is computed. The top 15 features contributing to CAD and stenosis
of the three main coronary arteries are determined. The relationship between stenosis of the main arteries is
presented using conditional probabilities. The effect of considering the number of stenotic arteries on sample
discrimination is investigated. The discrimination power over dataset samples is visualized, assuming each of the
three main coronary arteries as a sample label and considering the two remaining arteries as sample features
Hybrid image representation methods for automatic image annotation: a survey
In most automatic image annotation systems, images are represented with low level features using either global
methods or local methods. In global methods, the entire image is used as a unit. Local methods divide images into blocks where fixed-size sub-image blocks are adopted as sub-units; or into regions by using segmented regions as sub-units in images. In contrast to typical automatic image annotation methods that use either global or local features exclusively, several recent methods have considered incorporating the two kinds of information, and believe that the combination of the two levels of features is
beneficial in annotating images. In this paper, we provide a
survey on automatic image annotation techniques according to
one aspect: feature extraction, and, in order to complement
existing surveys in literature, we focus on the emerging image annotation methods: hybrid methods that combine both global and local features for image representation
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.
AlĂ©m disso, os Ășltimos anos revelam, claramente, uma tendĂȘncia 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 conteĂșdo, 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 soluçÔes nos fluxos de trabalho
é ainda rara e a eficåcia dos mais recentes sistemas de recuperação de
imagem médica pode ser melhorada.
A presente tese propÔe soluçÔes e métodos para recuperação de informação
multimodal, no contexto de repositórios de imagem médica. As contribuiçÔes
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 automåtica de imagens; e uma avaliação e
proposta de técnicas de representation learning para deteção automåtica de
conceitos em imagens médicas, exibindo maior potencial do que as técnicas
de extração de features visuais outrora pertinentes em tarefas semelhantes.
Estas contribuiçÔes 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 ĂĄrea
da saĂșde.Programa Doutoral em InformĂĄtic
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