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Visual Question Answering in the Medical Domain

Welcome to the 2nd edition of the Medical Domain Visual Question Answering Task!



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.


  • 09.11.2018: Website goes live.
  • 31.01.2019: Training and validation datasets released.
  • 18.03.2019: Test dataset released.

Task Description

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, we conducted a pilot task 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.


The datasets include a training set of 3,200 medical images with 12,792 Question-Answer (QA) pairs, a validation set of 500 medical images with 2,000 QA pairs, and a test set of 500 medical images with 500 questions. In order to generate a more-focused set of questions for a meaningful task evaluation, we considered generating 4 categories of questions based on: Modality, Plane, Organ System, and Abnormality. Please see the readme file of the crowdAI dataset section for more detailed information.

Evaluation Methodology

The following pre-processing methodology is applied before running the evaluation metrics on each answer:

  • Each answer is converted to lower-case
  • All punctuations are removed and the answer is tokenized to individual words

The evaluation would be conducted based on the following metrics:

  1. Accuracy (Strict)
    We use an adapted version of the accuracy metric from the general domain VQA task that considers exact matching of a participant provided answer and the ground truth answer.
  2. BLEU
    We use the BLEU metric [1] to capture the similarity between a system-generated answer and the ground truth answer. The overall methodology and resources for the BLEU metric are essentially similar to the ImageCLEF 2017 caption prediction task.
  3. References:

    [1] Papineni, K.; Roukos, S.; Ward, T.; Zhu, W. J. (2002). BLEU: a method for automatic evaluation of machine translation (PDF). ACL-2002: 40th Annual meeting of the Association for Computational Linguistics. pp. 311–318.

    Preliminary Schedule

  • 19.11.2018: Registration opens for all ImageCLEF tasks (until 26.04.2019)
  • 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 participants
  • 07.06.2019: Notification of acceptance of the working notes papers
  • 28.06.2019: Camera-ready working notes papers
  • 09-12.09.2019CLEF 2019, Lugano, Switzerland

Participant Registration

Please refer to the general ImageCLEF registration instructions

Submission Instructions

  • Each team is allowed to submit a maximum of 10 runs.
  • We expect the following format for the result submission file: <Image-ID><|><Answer>

    For example:

    rjv03401|answer of the first question in one single line
    AIAN-14-313-g002|answer of the second question
    wjem-11-76f3|answer of the third question

  • You need to respect the following constraints:

    • The separator between <Image-ID> and <Answer> has to be the pipe character (|).
    • Each <Image-ID> of the test set must be included in the run file exactly once.
    • All <Image-ID> must be present in a participant’s run file in the same order as the given test file.

  • Participants are allowed to use other resources asides from the official training/validation datasets, however the use of the additional resources must have to be explicitly stated. For meaningful comparison, we will separately group systems who exclusively use the official training data and who incorporate additional sources.


Details would be available later.


  • Asma Ben Abacha <asma.benabacha(at)>, National Library of Medicine, USA
  • Sadid A. Hasan <sadid.hasan(at)>, Philips Research Cambridge, USA
  • Vivek Datla <vivek.datla(at)>, Philips Research Cambridge, USA
  • Joey Liu <joey.liu(at)>, Philips Research Cambridge, USA
  • Dina Demner-Fushman <ddemner(at)>, National Library of Medicine, USA
  • Henning Müller <henning.mueller(at)>, University of Applied Sciences Western Switzerland, Sierre, Switzerland

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