Connectionists: (CfP) DEF-AI-MIA Workshop and 4th COV19D Competition in CVPR 2024

[Dimitrios Kollias]

Dear Colleagues,

We are inviting you to participate in the Domain adaptation, Explainability and Fairness in AI for Medical Image Analysis (DEF-AI-MIA) Workshop<https://ai-medical-image-analysis.github.io/4th> and 4th COV19D Competition<https://mlearn.lincoln.ac.uk/ai-mia-cov19d-competition/> to be held in conjunction with the IEEE Computer Vision and Pattern Recognition Conference (CVPR), 2024 in Seattle, USA, 17 – 21 June, 2024.



A) In the past few years, Deep Learning techniques have made rapid advances in many medical image analysis tasks. In pathology and radiology applications, they managed to increase the accuracy and precision of medical image assessment, which is often considered subjective and not optimally reproducible. This is due to the fact that they can extract more clinically relevant information from medical images than what is possible in current routine clinical practice by human assessors. Nevertheless, considerable development and validation work lies ahead before AI-based methods can be fully integrated ad used in routine clinical tasks.

Of major importance is research on domain adaptation, fairness and explainability in AI-enabled medical image analysis. This research constitutes the main target of the DEF-AI-MIA Workshop. The workshop aims to foster discussion and presentation of ideas to tackle these challenges in the field, as well as identify research opportunities in this context. More information can be found here<https://ai-medical-image-analysis.github.io/4th>.

Important Dates:

March 23, 2024: Paper submission
April 7, 2024:      Review decisions sent to authors; notifications of acceptance
April 14, 2024:    Camera ready version

For any requests or enquiries, please contact: stefanos at cs.ntua.gr


B) The 4th COV19D Competition includes two Challenges: i) Covid-19 Detection Challenge and ii) Covid-19 Domain Adaptation Challenge.
The 1st Challenge refers to detection of COVID-19 in chest 3D CT scans obtained from a single source, i.e., hospital. The 2nd Challenge refers to detection of COVID-19 in chest 3D CT scans obtained from various sources, i.e., hospitals with different data distributions.
More information can be found here<https://mlearn.lincoln.ac.uk/ai-mia-cov19d-competition/>.

Important Dates:

January 8, 2024: Opening of the Competition
March 16, 2024:  Submission of results
March 19, 2024: Winners announcement
March 23, 2024: Paper submission
April 7, 2024:    Review decisions sent to authors; notifications of acceptance
April 14, 2024:    Camera ready version


For registration and further information, please contact d.kollias at qmul.ac.uk


General Chairs:

Stefanos Kollias (National Technical University of Athens)
Dimitris N. Metaxas (Rutgers University)


Program Chairs:

Dimitrios Kollias (Queen Mary University London)
Xujiong Ye (University of Lincoln)
Francesco Rundo (STMicroelectronics ADG—Central R&D)


All accepted papers will be part of IEEE CVPR 2024 Conference Proceedings.



Kind Regards,
on behalf of the organising committee,
Dimitrios Kollias




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Dr Dimitrios Kollias, PhD, MIEEE, MBMVA, MAAAI, AMIARP, FHEA

Lecturer (Assistant Professor) in Artificial Intelligence

Member of Multimedia and Vision (MMV) research group

Member of Queen Mary Computer Vision Group

Member of Health Data in Practice (HDiP) Theme

Associate Member of Centre for Advanced Robotics (ARQ)
Academic Fellow of Digital Environment Research Institute (DERI)

School of EECS

Queen Mary University of London

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