ImageCLEF 2019

Motivation

ImageCLEF 2019 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.

For the 2019 edition, ImageCLEF organises 4 main tasks with a global objective of promoting the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains: lifelogging, medicine, nature, and security.

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; with special attention to the challenges of multi-modality, multi-linguality, and interactive search.

Stay tuned with us for the latest information and updates by joining us on the ImageCLEF social media accounts: Twitter @imageclef, and Facebook (click on the link).

ImageCLEF schedule

Each of the tasks sets its own schedule, so please check the corresponding task webpage for specific dates. A (tentative) global schedule can be found below:

• 12.11.2018: registration opens for all ImageCLEF tasks (until 26.04.2019)
• 12.11.2018: development data release starts (depends on the task)
• 18.03.2019: test data release starts (depends on the task)
• 01.05.2019: deadline for submitting the participants runs (depends on the task)
• 13.05.2019: release of the processed results by the task organizers (depends on the task)
• 24.05.2019: deadline for submission of working notes papers by the participants
• 07.06.2019: notification of acceptance of the working notes papers
• 28.06.2019: camera ready working notes papers
• 09-12.09.2019: CLEF 2019, Lugano, Switzerland

The CLEF Conference

ImageCLEF lab and all its tasks are part of the Conference and Labs of the Evaluation Forum: CLEF 2019. CLEF 2019 consists of an 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. Also there is more information about the Clef Initiative.

Programme of ImageCLEF at the CLEF 2019 Conference

(near final - small changes may still occur)


Plenary Session 1: Labs presentations
Monday 9 September

10:30-12:00

ImageCLEF 2019: Multimedia Retrieval in Medicine, Lifelogging, Security and Nature”,
Henning Müller, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland

Posters
Tuesday 10 September (with Lunch)

12:00-13:30

ImageCLEFcoral
1. “A two-staged Approach for Localization and Classification of Coral Reef Structures and Compositions”,
   Kirill Bogomasov, Heinrich-Heine-Universität Düsseldorf, Institut für Informatik, Germany
2. Automatic Classification of Coral Images using Colour and Textures”,
   Cristina Caridade, Instituto Politécnico de Coimbra, Portugal



ImageCLEFlifelog

3. “BIDAL@imageCLEFlifelog2019: The Role of Content and Context of Daily Activities in Insights from Lifelogs”,
   Minh-Son Dao, NICT, Japan
4. “LIFER 2.0: Discover Personal Lifelog Insight by Interactive Lifelog Retrieval System”,
   Liting Zhou, Dublin City University, Ireland
5. “HCMUS at ImageCLEFlifelog2019: Lifelog Moment Retrieval with Advanced Semantic Extraction and Flexible Moment Visualization for Exploration and Solving Life Puzzle with Visual Context-based Clustering and Habit Reference”,
   Nguyen-Khang Le & Trung-Hieu Hoang>, VNU-HCM University of Science, Vietnam



ImageCLEF-VQA-Med

6. “Deep Multimodal Learning for Medical Visual Question Answering”,
   Feifan Liu, University of Massachusetts Medical School, USA
7. “LSTM in VQA-Med, Is It Really Needed? Study on the ImageCLEF 2019 Dataset”,
   Assaf B. Spanier, Department of Software Engineering of Azrieli College of Engineering, Jerusalem
8. "Medical Visual Question Answering at Image CLEF 2019- VQA Med",
   Mohit Bansal, pwc, India



ImageCLEFmedCaption

9. “AUEB NLP Group at ImageCLEFmed Caption 2019”,
   John Pavlopoulos, Athens University of Economics and Business, Greece
10. “ImageSem at ImageCLEFmed Caption 2019 Task: a Two-stage Medical Concept Detection Strategy”,
    Zhen Guo, Institute of Medical Information, Chinese Academy of Medical Sciences, China
11. “Informative and Intriguing Visual Features in ImageCLEF Caption 2019”,
    Eduardo Pinho, Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Portugal
12. “Full Training Versus Fine Tuning for Radiology Images Concept Detection Task for the ImageCLEF 2019 Challenge“,
    Priyanshu Sinha, Indiana University Purdue University, USA



ImageCLEFmedTuberculosis

13. “Feature and Deep Learning Based Approaches for Automatic Report Generation and Severity Scoring of Lung Tuberculosis from CT Images”,
    Kirill Bogomasov, Heinrich-Heine-Universität Düsseldorf, Germany
14. “Lung Graph-Model Classification with SVM and CNN for Tuberculosis Severity Assessment and Automatic CT Report Generation”,
    Yashin Dicente, HES-SO Valais, Switzerland
15. “ImageCLEF 2019: CT Image Analysis for TB Severity Scoring and CT Report Generation using Autoencoded Image Features”,
    Siarhei Kazlouski, United Institute of Informatics Problems, Belarus
16. “ImageCLEF 2019: Projection-based CT Image Analysis for TB Severity Scoring and CT Report Generation”,
    Vitali Liauchuk, United Institute of Informatics Problems, Belarus
17. “Using improved Optical Flow Model to Detect Tuberculosis”,
    Fernando Llopis, University of Alicante, Spain
18. “Multi-View CNN with MLP for Diagnosing Tuberculosis Patients Using CT Scans and Clinically Relevant Metadata”,
    Abdela Ahmed Mossa, Çukurova University, Turkey
19. “Predicting Tuberculosis Related Lung Deformities from CT Scan Images Using 3D CNN”,
    Anup Pattnaik, PricewaterhouseCoopers US Advisory, India



ImageCLEFsecurity

20. “A Weighted Rule-Based Model for File Forgery Detection: UA.PT Bioinformatics at ImageCLEF 2019”,
    João R. Almeida, University of Aveiro, Portugal

S1 - Tuesday 10 September

13:30-15:00 ImageCLEF Overview of Tasks (6 x 15 minutes)

ImageCLEF-VQA-Med
1. “VQA-Med: Overview of the Medical Visual Question Answering Task at ImageCLEF 2019”,
   Asma Ben Abacha, Lister Hill Center, U.S. National Library of Medicine, USA
2. “Overview of the ImageCLEFmed 2019 Concept Prediction Task”,
   Obioma Pelka, University of Applied Sciences and Arts Dortmund, Germany
3. “Overview of the ImageCLEF 2019 Tuberculosis Task”,
   Yashin Dicente, HES-SO Valais, Switzerland
4. “Overview of ImageCLEFcoral 2019 Task”,
   Alba García Seco de Herrera, University of Essex, UK
5. “Overview of ImageCLEFlifelog 2019: Solve My Life Puzzle and Lifelog Moment Retrieval”,
   Duc-Tien Dang-Nguyen, University of Bergen, Norway
6. “Overview of the ImageCLEFsecurity 2019: File Forgery Detection Tasks”,
   Ergina Kavallieratou, University of the Aegean, Greece

S2 - Tuesday 10 September

15:30-16:30 ImageCLEF Visual Question Answering & Caption (4 x 15 minutes)

1. “Deep Multimodal Learning for Medical Visual Question Answering”,
   Feifan Liu, University of Massachusetts Medical School, USA
2. “LSTM in VQA-Med, Is It Really Needed? Study on the ImageCLEF 2019 Dataset”,
   Assaf B. Spanier, Department of Software Engineering of Azrieli College of Engineering, Jerusalem
3. "Medical Visual Question Answering at Image CLEF 2019- VQA Med",
   Mohit Bansal, pwc, India
4. “AUEB NLP Group at ImageCLEFmed Caption 2019”,
   John Pavlopoulos, Athens University of Economics and Business, Greece

S3 - Wednesday 11 September

15:30-16:30 ImageCLEF Caption, Tuberculosis & Lifelog (4 x 15 minutes)

1. “ImageSem at ImageCLEFmed Caption 2019 Task: a Two-stage Medical Concept Detection Strategy”,
   Zhen Guo, Institute of Medical Information, Chinese Academy of Medical Sciences, China
2. “ImageCLEF 2019: Projection-based CT Image Analysis for TB Severity Scoring and CT Report Generation”,
   Vitali Liauchuk, United Institute of Informatics Problems, Belarus
3. “Multi-View CNN with MLP for Diagnosing Tuberculosis Patients Using CT Scans and Clinically Relevant Metadata”,
   Abdela Ahmed Mossa, Çukurova University, Turkey
4. “BIDAL@imageCLEFlifelog2019: The Role of Content and Context of Daily Activities in Insights from Lifelogs”
   Minh-Son Dao, NICT, Japan

S4 - Wednesday 11 September

16:30-17:30 ImageCLEF Lifelog & Coral (4 x 15 minutes)

1. “Automated Lifelog Moment Retrieval based on Image Segmentation and Similarity Scores”,
   Stefan Taubert, Chemnitz University of Technology, Germany
2. “A two-staged Approach for Localization and Classification of Coral Reef Structures and Compositions”,
   Kirill Bogomasov, Heinrich-Heine-Universität Düsseldorf, Institut für Informatik, Germany
3. “Automatic Classification of Coral Images using Colour and Textures”,
   Cristina Caridade, Instituto Politécnico de Coimbra, Portugal
4. ImageCLEF feedback session. Everybody is invited to join.
   Moderator: Henning Müller, HES-SO Valais, Switzerland

Participant registration

1. Each participant has to register on (https://www.crowdai.org) with username, email and password. A representative team name should be used
   as username.
2. In order to be compliant with the CLEF requirements, participants also have to fill in the following additional fields on their profile:
   • First name
   • Last name
   • Affiliation
   • Address
   • City
   • Country

3. Participants now have to access the dataset tab, where they find a download link to the task's End User Agreement (EUA). At the same place they will also be able to upload a filled in and signed EUA.

   Participants have to fill in and submit one EUA for each ImageCLEF task they want to participate in. An ImageCLEF participant is considered as registered for a task as soon as he/she has uploaded a valid EUA that was approved by an ImageCLEF organizer (examples of how the EUAs for the medical task should be filled in can be found here: Caption EUA example, Tuberculosis EUA example and VQA EUA example).

   Registrations are handled on a per-task basis. This means if a task has multiple challenges (subtasks), a participant can automatically access the data of all challenges in that task because there is one common dataset per task. We do not separate datasets on a per-challenge basis.

PS: You do not have to remember all of the steps mentioned above as you will be given instructions on what to do as soon as you try to access a challenge's dataset tab.

The Tasks

ImageCLEF 2019 proposes 4 main tasks:

• ImageCLEFcoral: The increasing use of structure-from-motion photogrammetry for modelling large-scale environments from action cameras has driven the next generation of visualisation techniques. The task addresses the problem of automatically segmenting and labeling a collection of images of an underwater environment for the monitoring of coral reef structure and composition.
• ImageCLEFlifelog: An increasingly wide range of personal devices, such as smartphones, video cameras as well as wearable devices that allow capturing pictures, videos, and audio clips for every moment of our lives, are becoming available. In this context, the task addresses the problems of lifelogging data retrieval and summarization.
• ImageCLEFmedical: Medical images are used in a variety of scenarios. The task addresses the challenge of automatically predicting tuberculosis type from 3D chest CT scans and mapping of visual information to textual descriptions. The objective is to combine medical tasks into a common task with several subtasks to foster collaborations.
• ImageCLEFsecurity: File Forgery Detection is a very serious problem concerning digital forensics examiners. Fraud or counterfeits are common causes for altering files. Steganography is the practice of concealing a file, message, image or video within another file, message, image, or video. The task addresses the problems of automatically identifying forged content and retrieve hidden information.

The Organising Committee

Overall coordination

• Bogdan Ionescu <bionescu(at)alpha.imag.pub.ro>, University Politehnica of Bucharest, Romania
• Henning Müller <henning.mueller(at)hevs.ch>, University of Applied Sciences Western Switzerland, Sierre, Switzerland
• Renaud Péteri <renaud.peteri(at)univ-lr.fr>, University of La Rochelle, France

Technical support

• Ivan Eggel <ivan.eggel(at)hevs.ch>, University of Applied Sciences Western Switzerland, Sierre, Switzerland
• Mihai Dogariu <dogariu_mihai8(at)yahoo.com>, University Politehnica of Bucharest, Romania