[visionlist] ImageCLEF 2023 Multimedia Retrieval in CLEF Lab

Bogdan Ionescu bogdanlapi at gmail.com
Sun Jan 15 16:38:59 -04 2023

[Apologies for multiple postings]

ImageCLEF 2023
Multimedia Retrieval in CLEF


ImageCLEF 2023 is an evaluation campaign that is being organized as
part of the CLEF (Conference and Labs of the Evaluation Forum) labs.
The campaign offers several research tasks that welcome participation
from teams around the world.

The results of the campaign appear in the working notes proceedings,
published by CEUR Workshop Proceedings (CEUR-WS.org) and are presented
in the CLEF conference. Selected contributions among the participants
will be invited for submission to a special section "Best of CLEF'23
Labs" in the Springer Lecture Notes in Computer Science (LNCS) of
CLEF'23, together with the annual lab overviews.

Target communities involve (but are not limited to): information
retrieval (text, vision, audio, multimedia, social media, sensor data,
etc.), machine learning, deep learning, data mining, natural language
processing, image and video processing, computer vision, with special
attention to the challenges of multi-modality, multi-linguality, and
interactive search.

*** 2023 TASKS ***
- medical dialogue topic classification and summarization
- visual question answering and generation
- traceability of training data in synthetic medical image generation
- concept detection and caption prediction
- recommendations of articles and editorials from Europeana data
- classification of photographic user profiles in unintended scenarios
- late fusion mechanisms and ensembling

#ImageCLEFmedMEDIQA-Sum (new)
Clinical notes are documents that are routinely created by clinicians
after every patient encounter. They are used to record a patient's
health conditions as well as past or planned tests and treatments. The
task tackles the automatic generation of clinical notes summarizing
clinician-patient encounter conversations through dialogue to topic
classification, dialogue to note summarization, and full-encounter
dialogue to note summarization.

Organizers: Wen-wai Yim, and Asma Ben Abacha (Microsoft, USA), Neal
Snider (Microsoft/Nuance, USA), Griffin Adams (Columbia University,
USA), Meliha Yetisgen (University of Washington, USA).

#ImageCLEFmedVQA (new)
Identifying lesions in colonoscopy images is one of the most popular
applications of artificial intelligence in medicine. Until now, the
research has focused on single-image or video analysis. The main focus
of the task will be on visual question answering and visual question
generation. The goal is that through the combination of text and image
data the output of the analysis gets easier to use by medical experts.

Organizers: Michael A. Riegler, Steven A. Hicks, Vajira Thambawita,
Andrea Storås, and Pål Halvorsen (SimulaMet, Norway), Thomas de Lange,
Nikolaos Papachrysos, and Johanna Schöler (Sahlgrenska University
Hospital, Sweden), Debesh Jha (Norway & Northwestern University, USA).

#ImageCLEFmedGANs (new)
The task is focused on examining the existing hypothesis that GANs are
generating medical images that contain the "fingerprints" of the real
images used for generative network training. If the hypothesis is
correct, artificial biomedical images may be subject to the same
sharing and usage limitations as real sensitive medical data. On the
other hand, if the hypothesis is wrong, GANs may be potentially used
to create rich datasets of biomedical images that are free of ethical
and privacy regulations.

Organizers: Serge Kozlovski, and Vassili Kovalev (Belarusian Academy
of Sciences, Minsk, Belarus), Ihar Filipovich (Belarus State
University, Minsk, Belarus), Alexandra Andrei, Ioan Coman, and Bogdan
Ionescu (Politehnica University of Bucharest, Romania), Henning Müller
(University of Applied Sciences Western Switzerland, Sierre,

#ImageCLEFmedicalCaption (7th edition)
Interpreting and summarizing the insights gained from medical images
such as radiology output is a time-consuming task that involves highly
trained experts and often represents a bottleneck in clinical
diagnosis pipelines. The task addresses the need for automatic methods
that can approximate this mapping from visual information to condensed
textual descriptions. The more image characteristics are known, the
more structured are the radiology scans and hence, the more efficient
are the radiologists regarding interpretation.

Organizers: Johannes Rückert (University of Applied Sciences and Arts
Dortmund, Germany), Asma Ben Abacha (Microsoft, USA), Alba García Seco
de Herrera (University of Essex, UK), Christoph M. Friedrich
(University of Applied Sciences and Arts Dortmund, Germany), Henning
Müller (University of Applied Sciences Western Switzerland, Sierre,
Switzerland), Louise Bloch, Raphael Brüngel, Ahmad Idrissi-Yaghir, and
Henning Schäfer (University of Applied Sciences and Arts Dortmund,

#ImageCLEFrecommending (new)
In recent years cultural heritage organisations have made considerable
efforts to digitise their collections, and this trend is expected to
continue due to organisational goals and national cultural policies.
Thus media archives have not only exponentially increased in size, but
now hold contents in various modalities (video, image, text). Even
when structured metadata is available it is still difficult to
discover the contents of media archives and allow users to navigate
multiperspectivity in media collections. The task addresses the
content-based recommendation of meaningful articles and editorials for
specific topics from Europeana data.

Organizers: Alexandru Stan, and George Ioannidis (IN2 Digital
Innovations, Germany), Bogdan Ionescu (Politehnica University of
Bucharest, Romania), Hugo Manguinhas (Europeana Foundation,

#ImageCLEFaware (3rd edition)
The images available on social networks can be exploited in ways users
are unaware of when initially shared, including situations that have
serious consequences for the users’ real lives. For instance, it is
common practice for prospective employers to search online for
information about their future employees. This task addresses the
development of algorithms which raise the users’ awareness about
real-life impact of online image sharing by classifying user profiles
in a list of common unintended use-cases.

Organizers: Jérôme Deshayes-Chossart, and Adrian Popescu (CEA LIST,
France), Bogdan Ionescu (Politehnica University of Bucharest,

#ImageCLEFfusion (2nd edition)
Despite the current advances in knowledge discovery, single learners
do not produce satisfactory performance when dealing with complex
data, such as class imbalance, high-dimensionality, concept drift,
noisy data, multimodal data, etc. The task aims to fill this gap by
exploiting novel and innovative late fusion techniques for producing a
powerful learner based on the expertise of the pool of classifiers it
integrates. The task requires participants to develop aggregation
mechanisms of the outputs of the supplied systems and generate
ensemble predictions with significantly higher performance than the
individual systems.

Organizers: Liviu-Daniel Stefan, Mihai Gabriel Constantin, Mihai
Dogariu, and Bogdan Ionescu (Politehnica University of Bucharest,

(may vary depending on the task)
- Run submission: May 10, 2023
- Working notes submission: June 5, 2023
- CLEF 2023 conference: September 18-21, 2023, Thessaloniki, Greece

Follow the instructions here https://www.imageclef.org/2023.

Bogdan Ionescu, Politehnica University of Bucharest, Romania
Henning Müller, HES-SO, Sierre, Switzerland
Ana-Maria Dragulinescu, Politehnica University of Bucharest, Romania

The campaign is supported under the H2020 AI4Media “A European
Excellence Centre for Media, Society and Democracy” project, contract
#951911 https://www.ai4media.eu/.

On behalf of the organizers,

Bogdan Ionescu

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