[Apologies for multiple postings]
ImageCLEF 2023 Multimedia Retrieval in CLEF http://www.imageclef.org/2023/ https://www.facebook.com/ImageClef/ https://twitter.com/imageclef/
*** CALL FOR PARTICIPATION ***
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) https://www.imageclef.org/2023/medical/mediqa 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) https://www.imageclef.org/2023/medical/vqa 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) https://www.imageclef.org/2023/medical/gans 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, Switzerland).
#ImageCLEFmedicalCaption (7th edition) https://www.imageclef.org/2023/medical/caption 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, Germany).
#ImageCLEFrecommending (new) https://www.imageclef.org/2023/recommending 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, Netherlands).
#ImageCLEFaware (3rd edition) https://www.imageclef.org/2023/aware 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, Romania).
#ImageCLEFfusion (2nd edition) https://www.imageclef.org/2023/fusion 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, Romania).
*** IMPORTANT DATES *** (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
*** REGISTRATION *** Follow the instructions here https://www.imageclef.org/2023.
*** OVERALL COORDINATION *** Bogdan Ionescu, Politehnica University of Bucharest, Romania Henning Müller, HES-SO, Sierre, Switzerland Ana-Maria Dragulinescu, Politehnica University of Bucharest, Romania
*** ENDORSEMENT *** 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 https://www.aimultimedialab.ro/