Note: ImageCLEF Caption 2022 is divided into 2 subtasks (challenges). This is the Concept Detection challenge. For information on the Caption Prediction challenge click here. Both challenges' datasets are shared together, so registering for one of these challenges will automatically give you access to the other one.
Note: Do not forget to read the Rules section on this page. Pressing the red Participate button leads you to a page where you have to agree with those rules. You will not be able to submit any results before agreeing with the rules.
Note: Before trying to submit results, read the Submission instructions section on this page.
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.
Consequently, there is a considerable 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. We work on the basis of a large-scale collection of figures from open access biomedical journal articles (PubMed Central), as well as radiology images from original medical cases. All images in the training data are accompanied by UMLS concepts extracted from the original image caption.
- In the first and second editions of this task, held at ImageCLEF 2017 and ImageCLEF 2018, participants noted a broad variety of content and situation among training images. In 2019, the training data was reduced solely to radiology images, with ImageCLEF 2020 adding additional imaging modality information, for pre-processing purposes and multi-modal approaches
- The focus in ImageCLEF 2021 lay in using real radiology images annotated by medical doctors. This step aims at increasing the medical context relevance of the UMLS concepts
- For ImageCLEF 2022, an extended version of the ImageCLEF 2020 dataset is used
- To reduce the scope and size of concepts, several concept extraction tools are analyzed prior to caption pre-processing methods.
- Concepts with less occurrence will be removed
- As uncertainty regarding additional source was noted, we will clearly separate systems using exclusively the official training data from those that incorporate additional sources of evidence
The first step to automatic image captioning and scene understanding is identifying the presence and location of relevant concepts in a large corpus of medical images. Based on the visual image content, this challenge (subtask) provides the building blocks for the scene understanding step by identifying the individual components from which captions are composed. The concepts can be further applied for context-based image and information retrieval purposes.
Evaluation is conducted in terms of set coverage metrics such as precision, recall, and combinations thereof.
As soon as the data are released they will be available under the "Resources" tab.
A subset of the extended Radiology Objects in COntext (ROCO) dataset, for this edition without imaging modality information, is used for both subtasks. As in previous editions, the dataset originates from biomedical articles of the PMC OpenAccess subset.
Training Set: Consists of 83,275 radiology images
Validation Set: Consists of 7,645 radiology images
Test Set: Consists of 7601 radiology images
The concepts were generated using a reduced subset of the UMLS 2020 AB release, which includes the sections (restriction levels) 0, 1, 2, and 9. To improve the feasibility of recognizing concepts from the images, concepts were filtered based on their semantic type. Concepts with very low frequency were also removed, based on suggestions from previous years.
Evaluation is conducted in terms of F1 scores between system predicted and ground truth concepts, using the following methodology and parameters:
- The default implementation of the Python scikit-learn (v0.17.1-2) F1 scoring method is used. It is documented here.
- A Python (3.x) script loads the candidate run file, as well as the ground truth (GT) file, and processes each candidate-GT concept sets
- For each candidate-GT concept set, the y_pred and y_true arrays are generated. They are binary arrays indicating for each concept contained in both candidate and GT set if it is present (1) or not (0).
- The F1 score is then calculated. The default 'binary' averaging method is used.
- All F1 scores are summed and averaged over the number of elements in the test set (7'601), giving the final score.
The ground truth for the test set was generated based on the same reduced subset of the UMLS 2020 AB release which was used for the training data (see above for more details).
NOTE: The source code of the evaluation tool is available here. It must be executed using Python 3.x, on a system where the scikit-learn (>= v0.17.1-2) Python library is installed. The script should be run like this:
/path/to/python3 evaluate-f1.py /path/to/candidate/file /path/to/ground-truth/file
As soon as the submission is open, you will find a “Create Submission” button on this page (next to the tabs).
Before being allowed to submit your results, you have to first press the red participate button, which leads you to a page where you have to accept the challenge's rules.
Note: In order to participate in this challenge you have to sign an End User Agreement (EUA). You will find more information on the 'Resources' tab.
ImageCLEF lab is part of the Conference and Labs of the Evaluation Forum: CLEF 2022. CLEF 2022 consists of independent peer-reviewed workshops on a broad range of challenges in the fields of multilingual and multimodal information access evaluation, and a set of benchmarking activities carried in various labs designed to test different aspects of mono and cross-language Information retrieval systems. More details about the conference can be found here.
Submitting a working note with the full description of the methods used in each run is mandatory. Any run that could not be reproduced thanks to its description in the working notes might be removed from the official publication of the results. Working notes are published within CEUR-WS proceedings, resulting in an assignment of an individual DOI (URN) and an indexing by many bibliography systems including DBLP. According to the CEUR-WS policies, a light review of the working notes will be conducted by ImageCLEF organizing committee to ensure quality. As an illustration, ImageCLEF 2021 working notes (task overviews and participant working notes) can be found within CLEF 2021 CEUR-WS proceedings.
Participants of this challenge will automatically be registered at CLEF 2022. In order to be compliant with the CLEF registration requirements, please edit your profile by providing the following additional information:
Regarding the username, please choose a name that represents your team.
This information will not be publicly visible and will be exclusively used to contact you and to send the registration data to CLEF, which is the main organizer of all CLEF labs
Participating as an individual (non affiliated) researcher
We welcome individual researchers, i.e. not affiliated to any institution, to participate. We kindly ask you to provide us with a motivation letter containing the following information:
the presentation of your most relevant research activities related to the task/tasks
your motivation for participating in the task/tasks and how you want to exploit the results
a list of the most relevant 5 publications (if applicable)
the link to your personal webpage
The motivation letter should be directly concatenated to the End User Agreement document or sent as a PDF file to bionescu at imag dot pub dot ro. The request will be analyzed by the ImageCLEF organizing committee. We reserve the right to refuse any applicants whose experience in the field is too narrow, and would therefore most likely prevent them from being able to finish the task/tasks.
Information will be posted on https://www.imageclef.org/2022/medical/caption after the challenge ends.
ImageCLEF 2022 is an evaluation campaign that is being organized as part of the CLEF initiative 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). Selected contributions among the participants will be invited for publication in the following year in the Springer Lecture Notes in Computer Science (LNCS) together with the annual lab overviews.
- You can ask questions related to this challenge on the Discussion Forum. Before asking a new question please make sure that question has not been asked before.
- Click on Discussion tab above or direct link: https://www.aicrowd.com/challenges/imageclef-2022-caption-concept-detection/discussion
We strongly encourage you to use the public channels mentioned above for communications between the participants and the organizers. In extreme cases, if there are any queries or comments that you would like to make using a private communication channel, then you can send us an email at :
- johannes [dot] rueckert [at] fh-dortmund [dot] de
- abenabacha [at] microsoft [dot] com
- alba [dot] garcia [at] essex [dot] ac [dot] uk
You can find additional information on the challenge here: https://www.imageclef.org/2022/medical/caption