Workshop on

Domain adaptation, Explainability, Fairness

in AI for Medical Image Analysis


in conjunction with the IEEE Computer Vision and Pattern Recognition Conference (CVPR), 2024

17 June 2024


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.

The DEF-AI-MIA Workshop is the fourth in the AI-MIA series of Workshops held at IEEE ICASSP 2023, ECCV 2022 and ICCV 2021 Conferences.

For any requests or enquiries regarding the Workshop, please contact:


General Chairs

Dimitris N. Metaxas

Rutgers University USA

Stefanos Kollias

National Technical University of Athens, Greece

Program Chairs

Dimitrios Kollias

Queen Mary University of London, UK

Xujiong Ye

University of Lincoln, UK

Francesco Rundo

STMicroelectronics srl, Italy

The Workshop

Call for Papers

Original high-quality contributions (in terms of databases, surveys, studies, foundation models, techniques and methodologies) are solicited on -but are not limited to- the following topics and technologies:

    Explainable 2-D & 3D-CNN, RNN, CNN-RNN, Transformers, Foundation models, unimodal/multimodal Large Language Models (LLMs), unsupervised, self-supervised Machine Learning (ML) models for medical diagnosis

    Sensing “salient features” of AI/ML models related to decision-making, in spatial (images), temporal (video), volumetric (3-D) data

    Optimal visualization of salient features and areas in the input data

    Low/Middle/High level feature extraction & analysis for model interpretatability and explainability

    In temporal and 3-D data: explanation of which features and at what time, or slice, or respective intervals, are the most prominent for the provided decision

    In multimodal data: explainable data correlations for predictions in data streams

    Joint optimization of positive and negative saliencies

    Global and local models for prediction or classification

    Attention and self-attention mechanisms in DL/AI approaches

    Interpretability at training time through adversarial regularization

    Learning new data (from multiple sources) by leveraging knowledge already extracted and codified, through domain adaptation

    Generalizable ML/DL methods when the training medical image datasets are small

    Generalizable ML/DL methods in cases of images with potential domain shift

    Unsupervised, weakly supervised and semi-supervised model adaptation

    Uncertainty estimation and quantification, self-training

    Adaptation and prompt engineering in Foundation Models (e.g., LLMs) for explainable decisions and prediction

    Algorithmic fairness

    Zero- and one- shot learning, avoidance of catastrophic forgetting

Workshop Important Dates

Paper Submission Deadline (UPDATED):                                           23:59:59 AoE (Anywhere on Earth) March 29, 2024

Review decisions sent to authors; Notification of acceptance:       April 10, 2024

Camera ready version:                                                                       April 14, 2024

Submission Information

The paper format should adhere to the paper submission guidelines for main CVPR 2024 proceedings style. Please have a look at the Submission Guidelines Section here.

We welcome full long paper submissions (between 4 and 8 pages, excluding references or supplementary materials). All submissions must be anonymous and conform to the CVPR 2024 standards for double-blind review.

All papers should be submitted using this CMT website.

All accepted manuscripts will be part of CVPR 2024 conference proceedings.

The Competition

The 4th COV19D Competition is the 4th in the series of COV19D Competitions following the first 3 Competitions held in the framework of ICCV 2021, ECCV 2022 and ICASSP 2023 Conferences respectively. It includes two Challenges: i) Covid-19 Detection Challenge and ii) Covid-19 Domain Adaptation Challenge. More details can be found on this website.