Website: https://www.imageclef.org/2019/medical/vqa <http://www.imageclef.org/2018/VQA-Med>
Visual Question Answering is an exciting problem that combines natural language processing and computer vision techniques. Inspired by the recent success of visual question answering in the general domain <http://visualqa.org/>, we conducted a pilot task <https://www.imageclef.org/2018/VQA-Med> in ImageCLEF 2018 to focus on visual question answering in the medical domain. Based on the success of the inaugural edition and the huge interest from both computer vision and medical informatics communities, we will continue the task this year with enhanced focus on a nicely curated enlarged dataset. Same as last year, given a medical image accompanied with a clinically relevant question, participating systems are tasked with answering the question based on the visual image content.
With the increasing interest in artificial intelligence (AI) to support clinical decision making and improve patient engagement, opportunities to generate and leverage algorithms for automated medical image interpretation are currently being explored. Since patients may now access structured and unstructured data related to their health via patient portals, such access also motivates the need to help them better understand their conditions regarding their available data, including medical images.
The clinicians' confidence in interpreting complex medical images can be significantly enhanced by a “second opinion” provided by an automated system. In addition, patients may be interested in the morphology/physiology and disease-status of anatomical structures around a lesion that has been well characterized by their healthcare providers – and they may not necessarily be willing to pay significant amounts for a separate office- or hospital visit just to address such questions. Although patients often turn to search engines (e.g. Google) to disambiguate complex terms or obtain answers to confusing aspects of a medical image, results from search engines may be nonspecific, erroneous and misleading, or overwhelming in terms of the volume of information.
The data would consist of both medical images extracted from PubMed Central articles (essentially a subset of the ImageCLEF 2017 caption prediction task) and clinical images selected from the INDIANA dataset and MedPix® (VQA-RAD). Each image would have one or more question-answer pair(s) in the training and validation sets, and have only one question in the test set.
- *19.11.2018*: Registration opens for all ImageCLEF tasks (until
- *31.01.2019*: training and validation data release starts
- *18.03.2019*: Test data release starts
- *01.05.2019*: Deadline for submitting the participants runs
- *13.05.2019*: Release of the processed results by the task organizers
- *24.05.2019*: Deadline for submission of working notes papers by the
- *07.06.2019*: Notification of acceptance of the working notes papers
- *28.06.2019*: Camera-ready working notes papers
- *09-12.09.2019*: CLEF 2019 <http://clef2019.clef-initiative.eu/>,
Please refer to the general ImageCLEF registration instructions <https://www.imageclef.org/2019#registration>
- Asma Ben Abacha <https://sites.google.com/site/asmabenabacha/>
<asma.benabacha(at)nih.gov>, National Library of Medicine, USA
- Sadid A. Hasan <http://sadidhasan.com/> <sadid.hasan(at)philips.com>,
Philips Research Cambridge, USA
- Vivek Datla
<vivek.datla(at)philips.com>, Philips Research Cambridge, USA
- Joey Liu <https://scholar.google.com/citations?user=Gd-9UXEAAAAJ&hl=en>
<joey.liu(at)philips.com>, Philips Research Cambridge, USA
- Dina Demner-Fushman
mail.nih.gov>, National Library of Medicine, USA
- Henning Müller
<henning.mueller(at)hevs.ch>, University of Applied Sciences Western
Switzerland, Sierre, Switzerland
For more details and updates, please visit the task website at: https://www.imageclef.org/2019/medical/vqa <http://www.imageclef.org/2018/VQA-Med> and join our mailing list: https://groups.google.com/d/forum/imageclef-vqa-med .
*Sadid Hasan, PhD.* Senior Scientist, Artificial Intelligence Lab Philips Research North America Web: www.sadidhasan.com -------------- next part -------------- A non-text attachment was scrubbed... Name: not available Type: text/html Size: 10716 bytes Desc: not available URL: <https://mailman.uib.no/public/corpora/attachments/20181130/17eed562/attachment.txt>