The Parallel Distributed Image Search Engine (ParaDISE)

Image retrieval is a complex task that differs according to the context and the user requirements in any specific field, for example in a medical environment. Search by text is often not possible or optimal and retrieval by the visual content does not always succeed in modelling high-level concepts that a user is looking for. Modern image retrieval techniques consists of multiple steps and aim to retrieve information from large–scale datasets and not only based on global image appearance but local features and if possible in a connection between visual features and text or semantics. This paper presents the Parallel Distributed Image Search Engine (ParaDISE), an image retrieval system that combines visual search with text–based retrieval and that is available as open source and free of charge. The main design concepts of ParaDISE are flexibility, expandability, scalability and interoperability. These concepts constitute the system, able to be used both in real–world applications and as an image retrieval research platform. Apart from the architecture and the implementation of the system, two use cases are described, an application of ParaDISE in retrieval of images from the medical literature and a visual feature evaluation for medical image retrieval. Future steps include the creation of an open source community that will contribute and expand this platform based on the existing parts

Comparing Fusion Techniques for the ImageCLEF 2013 Medical Case Retrieval Task

Retrieval systems can supply similar cases with a proven diagnosis to a new example case under observation to help clinicians during their work. The ImageCLEFmed evaluation campaign proposes a framework where research groups can compare case-based retrieval approaches. This paper focuses on the case-based task and adds results of the compound figure separation and modality classification tasks. Several fusion approaches are compared to identify the approaches best adapted to the heterogeneous data of the task. Fusion of visual and textual features is analyzed, demonstrating that the selection of the fusion strategy can improve the best performance on the case-based retrieval task

The medGIFT Group in ImageCLEFmed 2013

This article presents the participation of the medGIFT groupin ImageCLEFmed 2013. Since 2004, the group has participated in themedical image retrieval tasks of ImageCLEF each year. There are fourtypes of tasks for ImageCLEFmed 2013: modality classi cation, image{based retrieval, case{based retrieval and a new task on compound gureseparation. The medGIFT group participated in all four tasks. MedGIFTis developing a system named ParaDISE (Parallel Distributed ImageSearch Engine), which is the successor of GIFT (GNU Image FindingTool). The alpha version of ParaDISE was used to run the experimentsin the competition. The focus was on the use of multiple features incombinations with novel strategies, i.e, compound gure separation formodality classi cation or modality ltering for ad{hoc image and case{based retrieval

Semi–Supervised Learning for Image Modality Classification

Searching for medical image content is a regular task for many physicians, especially in radiology. Retrieval of medical images from the scientific literature can benefit from automatic modality classification to focus the search and filter out non–relevant items. Training datasets are often unevenly distributed regarding the classes resulting sometimes in a less than optimal classification performance. This article proposes a semi–supervised learning approach applied using a k–Nearest Neighbour (k–NN) classifier to exploit unlabelled data and to expand the training set. The algorithmic implementation is described and the method is evaluated on the ImageCLEFmed modality classification benchmark. Results show that this approach achieves an improved performance over supervised k–NN and Random Forest classifiers. Moreover, medical case–based retrieval benefits from the modality filter

Crowdsourcing for Medical Image Classification

To help manage the large amount of biomedical images produced, image information retrieval tools have been developed to help access the right information at the right moment. To provide a test bed for image retrieval evaluation, the ImageCLEFmed benchmark proposes a biomedical classification task that automatically focuses on determining the image modality of figures from biomedical journal articles. In the training data for this machine learning task, some classes have many more images than others and thus a few classes are not well represented, which is a challenge for automatic image classification. To address this problem, an automatic training set expansion was first proposed. To improve the accuracy of the automatic training set expansion, a manual verification of the training set is done using the crowdsourcing platform Crowdflower. This platform allows the use of external persons to pay for the crowdsourcing or to use personal contacts free of charge. Crowdsourcing requires strict quality control or using trusted persons but it can quickly give access to a large number of judges and thus improve many machine learning tasks. Results show that the manual annotation of a large amount of biomedical images carried out in this project can help with image classification

Khresmoi Professional: Multilingual Semantic Search for Medical Professionals

There is increasing interest in and need for innovative solutions to medical search. In this paper we present the EU funded Khresmoi medical search and access system, currently in year 3 of 4 of development across 12 partners . The Khresmoi system uses a component based architecture housed in the cloud to allow for the development of several innovative applications to support target users medical information needs. The Khresmoi search systems based on this architecture have been designed to support the multilingual and multimod al information needs of three target groups the general public, general practitioners and consultant radiologists. In this paper we focus on the presentation of the systems to support the latter two groups using semantic, multilingual text and image based (including 2D and 3D radiology images) search

Khresmoi – multilingual semantic search of medical text and images

The Khresmoi project is developing a multilingual multimodal search and access system for medical and health information and documents. This scientific demonstration presents the current state of the Khresmoi integrated system, which includes components for text and image annotation, semantic search, search by image similarity and machine translation. The flexibility in adapting the system to varying requirements for different types of medical information search is demonstrated through two instantiations of the system, one aimed at medical professionals in general and the second aimed at radiologists. The key innovations of the Khresmoi system are the integration of multiple software components in a flexible scalable medical search system, the use of annotation cycles including manual correction to improve semantic search, and the possibility to do large scale visual similarity search on 2D and 3D (CT, MR) medical images

Khresmoi: Multimodal Multilingual Medical Information Search

Khresmoi is a European Integrated Project developing a multilingual multimodal search and access system for medical and health information and documents. It addresses the challenges of searching through huge amounts of medical data, including general medical information available on the internet, as well as radiology data in hospital archives. It is developing novel semantic search and visual search techniques for the medical domain. At the MIE Village of the Future, Khresmoi proposes to have two interactive demonstrations of the system under development, as well as an overview oral presentation and potentially some poster presentation

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

User-oriented medical image retrieval

This thesis studies the user-oriented design and development lifecycle of a medical image retrieval system. It investigates the user information needs of a target user group in Radiology. These needs are translated into requirements and specifications of a system that could assist radiologists to gain easy and quick access to medical information found in the open access literature and the internal hospital databases. The development of Parallel Distributed Image Search Engine is described and its architecture and applications are presented. The system is evaluated both empirically on the widely used ImageCLEF medical test challenge and on user tests with real users to assess its usability