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ImageCLEFmed MEDIQA-MAGIC

Motivation

The rapid development of telecommunication technologies, the increased demands for healthcare services, and recent pandemic needs, have accelerated the adoption of remote clinical diagnosis and treatment. In addition to live meetings with doctors which may be conducted through telephone or video, asynchronous options such as e-visits, emails, and messaging chats have also been proven to be cost-effective and convenient.

In this task, we focus on the problem of Multimodal And Generative TelemedICine (MAGIC) in the area of dermatology. Inputs will include text which give clinical context and queries, as well as one or more images. The challenge will tackle the generation an appropriate textual response to the query.

Consumer health question answering has been the subject of past challenges and research; however, these prior works only focus on text [1]. Previous work on visual question answering have focused mainly on radiology images and did not include additional clinical text input [2]. Also, while there is much work on dermatology image classification, much prior work is related to lesion malignancy classification for dermatoscope images [3].

This third edition of the MEDIQA-MAGIC task focuses on automatically generating segmentations and answers to common dermatological clinical questions, given textual clinical history, as well as user generated dermatology queries and images [4].

[1] Overview of the MEDIQA 2019 shared task on textual inference, question entailment and question answering. Asma Ben Abacha, Chaitanya Shivade, Dina Demner-Fushman. https://aclanthology.org/W19-5039/

[2] Vqa-med: Overview of the medical visual question answering task at imageclef 2019. Asma Ben Abacha , Sadid A. Hasan , Vivek V. Datla , Joey Liu , Dina Demner-Fushman, and Henning Muller. https://www.semanticscholar.org/paper/VQA-Med%3A-Overview-of-the-Medical...

[3] Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends. Zhouxiao Li, Konstantin Christoph Koban, Thilo Ludwig Schenck, Riccardo Enzo Giunta, Qingfeng Li, and Yangbai Sun. https://pubmed.ncbi.nlm.nih.gov/36431301/

[4] Overview of the MEDIQA-MAGIC Task at ImageCLEF 2024: Multimodal And Generative TelemedICine in Dermatology. Wen-wai Yim, Asma Ben Abacha, Yujuan Fu, Zhaoyi Sun, Meliha Yetisgen, Fei Xia. CLEF (Working Notes) 2024: 1456-1462 https://ceur-ws.org/Vol-3740/paper-133.pdf

Task Description

In the 3rd MEDIQA-MAGIC task, we focus on multimodal dermatology response generation. Participants will be given a clinical narrative context along with accompanying images.

Subtask 1: TBD

Subtask 2: TBD

Data

Information will be added soon.

Evaluation methodology

Information will be added soon.

Participant registration

Please refer to the general ImageCLEF registration instructions

Preliminary Schedule

  • TBD: Registration opens
  • TBD: Release of the training & validation sets of the Segmentation subtask
  • TBD: Release of the training & validation sets of the VQA subtask
  • TBD: Registration closes
  • TBD: Release of the test sets
  • TBD: Run submission deadline
  • TBD: Release of the processed results by the task organizers
  • TBD: Submission of participant papers [CEUR-WS]
  • TBD: Notification of acceptance
  • TBD: Camera ready copy of participant papers and extended lab overviews [CEUR-WS]
  • TBD: CLEF 2027, TBD

Submission Instructions

Results

CEUR Working Notes

Citations

Contact

Organizers:

  • Asma Ben Abacha, Microsoft
  • Wen-wai Yim, Microsoft
  • Noel Codella, Microsoft
  • Dr. Roberto Andres Novoa, Stanford University
  • Dr. Josep Malvehy, Hospital Clinic of Barcelona

For more information:

ImageCLEF - Cross-language image retrieval evaluations