3 research outputs found
Overview of the ImageCLEF 2018 Caption Prediction Tasks
The caption prediction task is in 2018 in its second edition after the task was first run in the same format in 2017. For 2018 the database was more focused on clinical images to limit diversity. As automatic methods with limited manual control were used to select images, there is still an important diversity remaining in the image data set.
Participation was relatively stable compared to 2017. Usage of external
data was restricted in 2018 to limit critical remarks regarding the use of
external resources by some groups in 2017. Results show that this is a
difficult task but that large amounts of training data can make it possible
to detect the general topics of an image from the biomedical literature.
For an even better comparison it seems important to filter the concepts
for the images that are made available. Very general concepts (such as “medical image”) need to be removed, as they are not specific for the
images shown, and also extremely rare concepts with only one or two
examples can not really be learned. Providing more coherent training data or larger quantities can also help to learn such complex models
Overview of ImageCLEF 2018: Challenges, Datasets and Evaluation
This paper presents an overview of the ImageCLEF 2018 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) Labs 2018. ImageCLEF is an ongoing initiative (it started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval with the aim of providing information access to collections of images in various usage scenarios and domains. In 2018, the 16th edition of ImageCLEF ran three main tasks and a pilot task: (1) a caption prediction task that aims at predicting the caption of a figure from the biomedical literature based only on the figure image; (2) a tuberculosis task that aims at detecting the tuberculosis type, severity and drug resistance from CT (Computed Tomography) volumes of the lung; (3) a LifeLog task (videos, images and other sources) about daily activities understanding and moment retrieval, and (4) a pilot task on visual question answering where systems are tasked with answering medical questions. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks, shows an increasing interest in this benchmarking campaign
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