Evaluating performance of biomedical image retrieval systems—An overview of the medical image retrieval task at ImageCLEF 2004–2013

Kalpathy–Cramer, Jayashree; Herrera, Alba García Seco de; Demner‐Fushman, Dina; Antani, Sameer; Bedrick, Steven; Müller, Henning

doi:10.1016/j.compmedimag.2014.03.004

Cited by 103 publications

(74 citation statements)

References 34 publications

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“…The medical task has run at ImageCLEF since 2004, with many changes between different editions [6]. The underlying objective of this challenge is the retrieval of similar images to fulfill a precise information need and image classification.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Evaluation campaigns have enabled the reproducible and comparative evaluation of new approaches, algorithms, theories and models through the use of standardized resources and common evaluation methodologies within regular and systematic evaluation cycles. The tasks organized over the years by Image-CLEF 1 [6] have provided an evaluation forum and framework for evaluating the state of the art in biomedical image retrieval.…”

Section: Introductionmentioning

confidence: 99%

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Medical image modality classification using discrete Bayesian networks

Arias

Martínez‐Gómez

Gámez

et al. 2016

Computer Vision and Image Understanding

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In this paper we propose a complete pipeline for medical image modality classification focused on the application of discrete Bayesian network classifiers. Modality refers to the categorization of biomedical images from the literature according to a previously defined set of image types, such as X-ray, graph or gene sequence. We describe an extensive pipeline starting with feature extraction from images, data combination, pre-processing and a range of different classification techniques and models. We study the expressive power of several image descriptors along with supervised discretization and feature selection to show the performance of discrete Bayesian networks compared to the usual deterministic classifier used in image classification. We perform an exhaustive experimentation by using the ImageCLEFmed 2013 collection. This problem presents a high number of classes so we propose several hierarchical approaches. In a first set of experiments we evaluate a wide range of parameters for our pipeline along with several classification models. Finally, we perform a comparison by setting up the competition environment between our selected approaches and the best ones of the original competition. Results show that the Bayesian Network classifiers obtain very competitive results. Furthermore, the proposed approach is stable and it can be applied to other problems that present inherent hierarchical structures of classes.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Medical image modality classification using discrete Bayesian networks

Arias

Martínez‐Gómez

Gámez

et al. 2016

Computer Vision and Image Understanding

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“…Kajian oleh Kalpathy-Cramer et al menyatakan ketepatan keputusan dapatan semula imej boleh dicapai dengan bantuan teknik-teknik yang digunakan oleh CADS (Kalpathy-Cramer et al 2015). Selain membantu dalam proses diagnosis, sistem DSI juga bertujuan untuk membantu pakar radiologi dengan memberikan maklumat yang sesuai (Ahad et al 2011) dan berkaitan kes yang sedang dirawat.…”

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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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“…Now it is getting easier to access data collections but it is still hard to obtain annotated data with a clear evaluation scenario and strong baselines to compare to. Motivated by this, ImageCLEF has for 15 years been an initiative that aims at evaluating multilingual or language independent annotation and retrieval of images [15,18,5,24]. The main goal of ImageCLEF is to support the advancement of the field of visual media analysis, classification, annotation, indexing and retrieval.…”

Section: Introductionmentioning

confidence: 99%

Overview of ImageCLEF 2017: Information Extraction from Images

Ionescu

Müller

Villegas³

et al. 2017

Lecture Notes in Computer Science

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Abstract. This paper presents an overview of the ImageCLEF 2017 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) labs 2017. ImageCLEF is an ongoing initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval for providing information access to collections of images in various usage scenarios and domains. In 2017, the 15th edition of ImageCLEF, three main tasks were proposed and one pilot task: 1) a LifeLog task about searching in LifeLog data, so videos, images and other sources; 2) a caption prediction task that aims at predicting the caption of a figure from the biomedical literature based on the figure alone; 3) a tuberculosis task that aims at detecting the tuberculosis type from CT (Computed Tomography) volumes of the lung and also the drug resistance of the tuberculosis; and 4) a remote sensing pilot task that aims at predicting population density based on satellite images. The strong participation of over 150 research groups registering for the four tasks and 27 groups submitting results shows the interest in this benchmarking campaign despite the fact that all four tasks were new and had to create their own community.

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Evaluating performance of biomedical image retrieval systems—An overview of the medical image retrieval task at ImageCLEF 2004–2013

Cited by 103 publications

References 34 publications

Medical image modality classification using discrete Bayesian networks

Medical image modality classification using discrete Bayesian networks

Content-based Image Retrieval for Medical Application

Overview of ImageCLEF 2017: Information Extraction from Images

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