MICCAI Society Announces new SIGs

The MICCAI Society, through the Education Working Group, financially supports the creation of Special Interest Groups, or SIGs, to increase collaboration, activities, and the exchange of ideas in a specific area of research related to medical image computing and computer assisted intervention. SIGs are managed independently by a board of directors drawn from the MICCAI Society who are representatives from the academic, industrial, and clinical communities, and experts in the SIG’s specific area of interest.

We are happy to announce that the MICCAI Society has accepted proposals to establish and support three new SIGs. If you would like to learn more about a SIG or would like to get involved, please reach out to the contact person provided.

SIG-FAIMI: Special Interest Group on Fairness of AI on Medical Imaging

During the last few years, the research community of fairness, equity and accountability in machine learning has highlighted the potential risks associated with biased systems in various application scenarios, ranging from face recognition to neural translation models and job hiring assistants. A large body of research studies has shown that, due to a variety of reasons, such as database construction, modelling choices, training strategies and even lack of diversity in team composition, such machine learning systems can be biased in terms of demographic attributes like gender, ethnicity, age or geographical distribution, presenting unequal behaviour on disadvantaged or underrepresented subpopulations. Even though fairness in machine learning has been extensively studied in decision-making scenarios like job hiring, credit scoring and criminal justice, as well as computer vision applications, it was not until recently that researchers started to study and characterize bias and design mitigation strategies for systems in medical image computing (MIC) and computer assisted interventions (CAI).  

The SIG-FAIMI aims to create awareness about potential fairness issues that can emerge in the context of computerized medical imaging and computer assisted interventions. It will act as a forum within the MICCAI community to discuss such issues from a comprehensive perspective, including not only methodological advances to diagnose and mitigate fairness biases but also to understand its consequences in the clinical context, generate awareness in the medical imaging community, and provide a venue to generate consensus and guidelines on how to address them. 

Goals: 

• Targeted engagement of active groups around fairness in AI, both internal and external to MICCAI.  
• Promote research within the MICCAI community on topics related to fairness of AI in medical imaging and create awareness about the importance of incorporating fairness aspects in our daily research practices. 
• Expand the MICCAI community and conference attendees to, but not limited to, legal and ethics experts. This will include reaching out to experts on the areas of ethics and legal/regulatory considerations of fairness. 
• Increase the diversity of the MICCAI workshops for those interested, by providing support, structure and access to the wider scientific community. 
• Create effective communication among the different communities with a web page listing useful resources to learn about fairness of AI in medical imaging, and use of social media. 

FAIMI 2024: Call for Papers and Reviewers

SIG-FAIMI is hosting the second Fairness of AI in Medical Imaging (FAIMI) Workshop at MICCAI 2024. Papers that explore the critical issues of fairness, bias and ethical implications of AI in healthcare can be submitted until June 24, 2024.  More information, including detailed submission instructions, are available on the workshop website.

Keynote presentation: Dr. Mercy Asiedu about “Machine Learning Fairness for Health in Africa”.

Reviewers are also needed! If you would like to become a reviewer, please complete this application form.

Contact:

Esther Puyol Anton, President
esther.puyol_anton@kcl.ac.uk

SIG-Cardiac: Special Interest Group on Cardiac Imaging, Computational Modelling and Clinical Sciences

The Special Interest Group (SIG) in Cardiac Imaging, Computational Modelling and Clinical Sciences (SIG-Cardiac) intends to bring together cardiologists, radiologists, computer scientists, and engineers to promote interdisciplinary collaboration.

The mission of SIG-Cardiac is to become a dynamic and innovative forum within the MICCAI Society, dedicated to advancing cardiac care through the clinical application of research technologies. By bridging the gap between cutting-edge research and clinical practice, SIG-Cardiac ensures that technological advancements directly contribute to improved patient outcomes and more efficient clinical workflows. The vision is to create a future where cardiac care consistently benefits from the seamless integration of research innovations, ultimately enhancing the well-being of patients, supporting clinicians, and positively impacting the broader healthcare system.

Goals:

1. Foster Interdisciplinary Collaboration: Connect cardiologists, radiologists, computer scientists, and engineers to promote interdisciplinary collaboration. Recognizing the vital role each discipline plays, SIG-Cardiac aims to leverage collective expertise for the advancement of cardiac care.

2. Integrate Research Breakthroughs: Facilitate the integration of emerging technologies, such as AI, computer vision, and advanced imaging techniques, into clinical practice. By encouraging the adoption of these breakthroughs, SIG-Cardiac strives to enhance cardiac diagnostics, treatment planning, and improve patient outcomes.

3. Educate and Train: Organize challenges and tutorials at MICCAI conferences to educate and train clinicians and researchers. These initiatives are designed to enable and facilitate the latest technologies to be deployed in cardiac care.

Contact:

Dr. YingLiang Ma
Yingliang.ma@uea.ac.uk

SIG-xMedIA: Special Interest Group on Explainable AI for Medical Image Analysis

The primary mission of SIG-xMedIA is to strengthen the exchange between research groups focused on explainable AI for real-world medical image analysis. By organizing satellite events at MICCAI conferences and biennial international workshops, SIG-xMedIA aims to:

1. Facilitate Collaboration: Provide platforms for researchers to collaborate, share insights, and collectively drive advancements in XAI.
2. Curate Datasets: Organize challenges like CARE (http://zmic.org.cn/care_2024/), curating standard and public real-world medical image datasets for model development and evaluation.
3. Financial Support for Education: Support tutorials, workshops, and educational initiatives to encourage diverse participation and foster best practices in the field.

Through these initiatives, SIG-xMedIA aspires to make significant contributions to the development of XAI, promoting openness, transparency, and community-driven progress in real-world medical image analysis.

Goals:

1. Establish a Specialized Community:

• Create a global community of researchers and clinicians dedicated to advancing XAI in medical image analysis.
• Connect diverse perspectives and disciplines to collectively address challenges and opportunities in the field.

2. Promote XAI in MedIA:

• Facilitate collaboration among research groups to advance the development of explainable AI in real-world medical image analysis, which also sustains high consensus with clinicians.
• Encourage consensus-building on principles guiding model design and evaluation.
  
  3. Provide a Platform for Collaboration and Education:
• Create a platform for the exchange of ideas of explaining AI, methodologies of understanding decision-making process, and clinical practices of interpreting disease causes.
• Foster collaboration through satellite events, workshops, and challenges, ensuring an open and reliable stage for evaluations and training.
• Promote educational initiatives and opportunities in xMedIA to student communities by Student Board

Contact: 

Dr. Shangqi Gao, University of Cambridge
sg2162@cam.ac.uk