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Background: The introduction of digital pathology into clinical practice has led to the development of clinical workflows with digital images, in connection with pathology reports. Still, most of the current work is time-consuming manual analysis of image areas at different scales. Links with data in the biomedical literature are rare, and a need for search based on visual similarity within whole slide images (WSIs) exists. Objectives: The main objective of the work presented is to integrate content-based visual retrieval with a WSI viewer in a prototype. Another objective is to connect cases analyzed in the viewer with cases or images from the biomedical literature, including the search through visual similarity and text. Methods: An innovative retrieval system for digital pathology is integrated with a WSI viewer, allowing to define regions of interest (ROIs) in images as queries for finding visually similar areas in the same or other images and to zoom in/out to find structures at varying magnification levels. The algorithms are based on a multimodal approach, exploiting both text information and content-based image features. Results: The retrieval system allows viewing WSIs and searching for regions that are visually similar to manually defined ROIs in various data sources (proprietary and public datasets, e.g., scientific literature). The system was tested by pathologists, highlighting its capabilities and suggesting ways to improve it and make it more usable in clinical practice. Conclusions: The developed system can enhance the practice of pathologists by enabling them to use their experience and knowledge to control artificial intelligence tools for navigating repositories of images for clinical decision support and teaching, where the comparison with visually similar cases can help to avoid misinterpretations. The system is available as open source, allowing the scientific community to test, ideate and develop similar systems for research and clinical practice. [ABSTRACT FROM AUTHOR]

A growing number of clinicians, educators, researchers, and others use digital images in their work and search for them via image retrieval systems. Yet, this area of information retrieval is much less understood and developed than searching for text-based content, such as biomedical literature and its derivations. The goal of the ImageCLEF medical image retrieval task (ImageCLEFmed) is to improve understanding and system capability in search for medical images. In this paper, we describe the development and use of a medical image test collection designed to facilitate research with image retrieval systems and their users. We also provide baseline results with the new collection and describe them in the context of past research with portions of the collection. [ABSTRACT FROM AUTHOR]

In this paper, the automatic annotation task of the 2005 CLEF cross-language image retrieval campaign (ImageCLEF) is described. This paper focuses on the database used, the task setup, and the plans for further medical image annotation tasks in the context of ImageCLEF. Furthermore, a short summary of the results of 2005 is given. The automatic annotation task was added to ImageCLEF in 2005 and provides the first international evaluation of state-of-the-art methods for completely automatic annotation of medical images based on visual properties. The aim of this task is to explore and promote the use of automatic annotation techniques to allow for extracting semantic information from little-annotated medical images. A database of 10.000 images was established and annotated by experienced physicians resulting in 57 classes, each with at least 10 images. Detailed analysis is done regarding the (i) image representation, (ii) classification method, and (iii) learning method. Based on the strong participation of the 2005 campain, future benchmarks are planned. [ABSTRACT FROM AUTHOR]

Content-based image retrieval is starting to become an increasingly important factor in medical imaging research and image management systems. Several retrieval systems and methodologies exist and are used in a large variety of applications from automatic labelling of images to diagnostic aid and image classification. Still, it is very hard to compare the performance of these systems as the used databases often contain copyrighted or private images and are thus not interchangeable between research groups, also for patient privacy. Most of the currently used databases for evaluating systems are also fairly small which is partly due to the high cost in obtaining a gold standard or ground truth that is necessary for evaluation. Several large image databases, though without a gold standard, start to be available publicly, for example by the NIH (National Institutes for Health).This article describes the creation of a large medical image database that is used in a teaching file containing more than 8700 varied medical images. The images are anonymised and can be exchanged free of charge and copyright. Ground truth (a gold standard) has been obtained for a set of 26 images being selected as query topics for content-based query by image example. To reduce the time for the generation of ground truth, pooling methods well known from the text or information retrieval field have been used. Such a database is a good starting point for comparing the current image retrieval systems and to measure the retrieval quality, especially within the context of teaching files, image case databases and the support of teaching. For a comparison of retrieval systems for diagnostic aid, specialised image databases, including the diagnosis and a case description will need to be made available, as well, including gold standards for a proper system evaluation.A first evaluation event for image retrieval is foreseen at the 2004 CLEF conference (Cross Language Evaluation Forum) to compare text-and content-based access mechanism to images. [Copyright &y& Elsevier]

This article describes an approach to learn feature weights for content-based image retrieval (CBIR) from user interaction log files. These usage log files are analyzed for images marked together by a user in the same query step. The problem is somewhat similar to one of the traditional data mining problems, the market basket analysis problem, where items bought together in a supermarket are analyzed. This paper outlines similarities and differences between the two fields and explains how to use the interaction data for deriving a better feature weighting. Experiments with existing log files are done and a significant improvement in performance is reached with a feature weighting calculated from the information contained in the log files. Even with several steps of relevance feedback the results remain much better than without the learning, which means that not only information from feedback is taken into account earlier, but a better quality of retrieval is reached in all steps. [ABSTRACT FROM AUTHOR]

Content-based histopathology image retrieval can be a useful technique for help in diagnosing various diseases. The process of retrieving images is often time-consuming and challenging due to the need for high-dimensional features when trying to model complex content. Hashing methods can therefore be employed to resolve the challenge by producing binary codes of different lengths. Deep hashing methods are frequently superior to traditional machine learning approaches but are affected by the size of training sets. In addition, back-propagation learning can further complicate the generation of binary values. Hence, this paper proposes a novel Siamese deep hashing model, named histopathology Siamese deep hashing (HSDH), for histopathology image retrieval. Two designed deep hashing models with shared weights and structures are used to generate hash codes. A Hamming distance layer is then applied to evaluate the similarity of the generated values. A highly effective loss function is also introduced that incorporates a modified version of the standard contrastive loss function with an error estimation term to improve both the training and retrieval phases. In the retrieval phase, the trained model compares a query image with all the training images and ranks the most similar images. According to the experimental results on two publicly available databases, BreakHis and Kather, the HSDH model outperforms other state-of-the-art hashing-based methods in histopathology image retrieval. [ABSTRACT FROM AUTHOR]

Purpose This article reports the user-oriented evaluation of a text- and content-based medical image retrieval system. User tests with radiologists using a search system for images in the medical literature are presented. The goal of the tests is to assess the usability of the system, identify system and interface aspects that need improvement and useful additions. Another objective is to investigate the system's added value to radiology information retrieval. The study provides an insight into required specifications and potential shortcomings of medical image retrieval systems through a concrete methodology for conducting user tests. Methods User tests with a working image retrieval system of images from the biomedical literature were performed in an iterative manner, where each iteration had the participants perform radiology information seeking tasks and then refining the system as well as the user study design itself. During these tasks the interaction of the users with the system was monitored, usability aspects were measured, retrieval success rates recorded and feedback was collected through survey forms. Results In total, 16 radiologists participated in the user tests. The success rates in finding relevant information were on average 87% and 78% for image and case retrieval tasks, respectively. The average time for a successful search was below 3 min in both cases. Users felt quickly comfortable with the novel techniques and tools (after 5 to 15 min), such as content-based image retrieval and relevance feedback. User satisfaction measures show a very positive attitude toward the system's functionalities while the user feedback helped identifying the system's weak points. The participants proposed several potentially useful new functionalities, such as filtering by imaging modality and search for articles using image examples. Conclusion The iterative character of the evaluation helped to obtain diverse and detailed feedback on all system aspects. Radiologists are quickly familiar with the functionalities but have several comments on desired functionalities. The analysis of the results can potentially assist system refinement for future medical information retrieval systems. Moreover, the methodology presented as well as the discussion on the limitations and challenges of such studies can be useful for user-oriented medical image retrieval evaluation, as user-oriented evaluation of interactive system is still only rarely performed. Such interactive evaluations can be limited in effort if done iteratively and can give many insights for developing better systems. [ABSTRACT FROM AUTHOR]

Medical image retrieval and classification have been extremely active research topics over the past 15 years. Within the ImageCLEF benchmark in medical image retrieval and classification, a standard test bed was created that allows researchers to compare their approaches and ideas on increasingly large and varied data sets including generated ground truth. This article describes the lessons learned in ten evaluation campaigns. A detailed analysis of the data also highlights the value of the resources created. [ABSTRACT FROM AUTHOR]