5 research outputs found
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,
clínicas e outros centros de diagnóstico, levou à criação de enormes
repositórios de dados, frequentemente não 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 médica, o que traz benefícios para os médicos 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 são as seguintes: um motor de pesquisa para estudos de imagem
médica com suporte a pesquisas multimodais num arquivo extensível; 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 técnicas e científicas
para o desenvolvimento e adoção de sistemas modernos de recuperação de
imagem médica multimodal, de modo a que estes façam finalmente parte
das ferramentas típicas dos profissionais, professores e investigadores da área
da saúde.Programa Doutoral em Informátic
Atas das Oitavas Jornadas de Informática da Universidade de Évora
Atas das Oitavas Jornadas de Informática da Universidade de Évora realizadas em Março de 2018
Tuberculosis diagnosis from pulmonary chest x-ray using deep learning.
Doctoral Degree. University of KwaZulu-Natal, Durban.Tuberculosis (TB) remains a life-threatening disease, and it is one of the leading
causes of mortality in developing countries. This is due to poverty and inadequate
medical resources. While treatment for TB is possible, it requires an accurate diagnosis
first. Several screening tools are available, and the most reliable is Chest
X-Ray (CXR), but the radiological expertise for accurately interpreting the CXR
images is often lacking. Over the years, CXR has been manually examined; this
process results in delayed diagnosis, is time-consuming, expensive, and is prone
to misdiagnosis, which could further spread the disease among individuals. Consequently,
an algorithm could increase diagnosis efficiency, improve performance,
reduce the cost of manual screening and ultimately result in early/timely diagnosis.
Several algorithms have been implemented to diagnose TB automatically. However,
these algorithms are characterized by low accuracy and sensitivity leading to misdiagnosis.
In recent years, Convolutional Neural Networks (CNN), a class of Deep
Learning, has demonstrated tremendous success in object detection and image classification
task. Hence, this thesis proposed an efficient Computer-Aided Diagnosis
(CAD) system with high accuracy and sensitivity for TB detection and classification.
The proposed model is based firstly on novel end-to-end CNN architecture,
then a pre-trained Deep CNN model that is fine-tuned and employed as a features
extractor from CXR. Finally, Ensemble Learning was explored to develop an
Ensemble model for TB classification. The Ensemble model achieved a new stateof-
the-art diagnosis accuracy of 97.44% with a 99.18% sensitivity, 96.21% specificity
and 0.96% AUC. These results are comparable with state-of-the-art techniques and
outperform existing TB classification models.Author's Publications listed on page iii
Deep Learning in Medical Image Analysis
The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis