ImageCLEF 2021 Caption - Caption Prediction
Note: ImageCLEF Caption 2021 is divided into 2 subtasks (challenges). This is the Caption Prediction challenge. For information on the Concept Detection challenge click here. Both challenges dataset are shared together, so registering for one of these challenges will automatically give you access to the other one.
Note: ImageCLEF 2021 Caption is part of the official ImageCLEF 2021 medical task. Here is a list of other ImageCLEF 2021 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 characteristic is 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
In ImageCLEF 2020 the focus remained on radiology images, with additional imaging modality information, for pre-processing purposes and multi-modal approaches
The focus in ImageCLEF 2021 lies in using real radiology images annotated by medical doctors. This step aims at increasing the medical context relevance of the UMLS concepts.
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 sources was noted, we will clearly separate systems using exclusively the official training data from those that incorporate additional sources of evidence.
Caption Prediction Task
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 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. This task will be run using real clinical radiology images with annotations from medical doctors.
As soon as the data is released it will be available under the "Resources" tab.
- Development data will be released on 26.02.2021
- Test data will be released on 29.04.2021
As soon as the submission is open, you will find a “Create Submission” button on this page.
1. Click on the red 'create submission' button at the top right next to the horizontal menu tabs. If the button is not there please make sure that your EUA was accepted and that you also hit the participate button before.
2. Fill in the required information and select a file to submit. Then hit submit.
3. You will now land on the submissions page and should be able to see the submission status. It will show your score or an error message (if there was a framework error or validation error). The status does not automatically get refreshed, so you have to reload the page to see updates to the status.
- The separator between the figure ID and the description has to be a pipe charachter ( | )
- Each figure ID of the testset must be included in the runfile exactly once
- You should not include special characters in the description.
Please note that each group is allowed a maximum of 10 runs
Evaluation is based on BLEU scores, using the following methodology and parameters:
The default implementation of the Python NLTK (v3.2.2) (Natural Language ToolKit) BLEU scoring method is used.
The caption is converted to lower-case
All punctuation is removed an the caption is tokenized into its individual words
Stopwords are removed using NLTK's "english" stopword list
Stemming is applied using NLTK's Snowball stemmer
The BLEU score is then calculated. Note that the caption is always considered as a single sentence, even if it actually contains several sentences. No smoothing function is used.
All BLEU scores are summed and averaged over the number of captions, giving the final score.
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 2021. CLEF 2021 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 2020 working notes (task overviews and participant working notes) can be found within CLEF 2020 CEUR-WS proceedings.
Participants of this challenge will automatically be registered at CLEF 2021. 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 2021 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-2021-caption-caption-prediction/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 :
- contact address go here
Submission Format:More information
You can find additional information on the challenge here: https://www.imageclef.org/2021/caption