ImageCLEF 2020 Caption - Concept Detection
Note: ImageCLEF 2020 Caption is part of the official ImageCLEF 2020 medical task. Here is a list of other ImageCLEF 2020 medical task challenges:
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). 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
The focus of the ImageCLEF 2020 is on radiology images, with additional imaging modality information, for pre-processing purposes and multi-modal approaches
A large number of concepts were used in previous years. This year, the captions are first processed before concept extraction, hence leading to a reduced number of concepts.
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
As soon as the data is released it will be available under the “Resources” tab.
- Development data will be released on 31.01.2020
- Test data will be released on 27.03.2020
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 rules of the challenge.
Please note that each group is allowed a maximum of 10 runs per subtask.
For the submission of the concept detection task we expect the following format: - ROCO_CLEF_41341 C0033785;C0035561 - ROCO_CLEF_07563 C0043299;C1306645;C1548003;C1962945.
You need to respect the following constraints:
The separator between the figure ID and the concepts has to be a tabular whitespace
The separator between the UMLS concepts has to be a semicolon (;)
Each figure ID of the test set must be included in the submitted file exactly once (even if there are not concepts)
The same concept cannot be specified more than once for a given figure ID
The maximum number of concepts per image is 100
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 (3500), giving the final score.
The ground truth for the test set was generated based on the UMLS Full Release 2019AB.
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
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 2020. CLEF 2020 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 2019 working notes (task overviews and participant working notes) can be found within CLEF 2019 CEUR-WS proceedings.
Participants of this challenge will automatically be registered at CLEF 2020. 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 after the challenge ends.
ImageCLEF 2020 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://discourse.aicrowd.com/c/imageclef-2020-caption-concept-detection
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 :
You can find additional information on the challenge here: https://www.imageclef.org/2020/medical/caption