Call for Papers

Regular Papers

We invite you to submit your contributions (regular long paper —12-page limit) that address medical problems of emerging and developing countries via algorithms spanning different sub-fields including but not limited to:

• Image to Image Translation (e.g., going from low image quality of low-cost device to high image quality)

• Annotation-efficient DL Models (e.g., unsupervised, semi-supervised,...etc.)

• Handling data heterogeneity (e.g., missing and noisy data)

• Domain Adaptation and Transfer Learning

• Continual and Meta-Learning

• Bias-resilient and Fairness (e.g., measures to identify biases)

• Model Compactness and Compression for limited energy and lower-end devices

• Interpretable and Trustable AI Models

• Multimodal data (Imaging, Biosignal, EHR/EMR, Genomics, multi-Omics)

With a sharp focus on limited resources areas of applications in medicine such as:

• Limited data generated by low infrastructure (e.g., poor quality, low resolution, missing slices, incomplete scans, communication bandwidth issues challenging bulky data transmission, regulatory hurdles to sharing data on cloud,…etc.)

• Basic imaging modalities/facilities (e.g., X-rays, ultrasound, retinal scans, microscopy, Optical imaging, e.g. Skin Lesion, Fundus,…etc.)

• Low-cost portable cameras and smart-phone based camera imaging and videos for diagnosis

• Biosignals (e.g., stethoscope, EEG, ECG,…etc.)

• Minimal medical and computational resources for diagnosis using basic imaging facilities

Regular Papers will be included in the proceedings of our workshop as part of the MICCAI Satellite Events joint LNCS proceedings.

White Papers

Besides, we are also accepting white papers (4-6 page limit), focusing on:

• Introducing and identifying the AI challenges/opportunities in healthcare with low resources

• Presenting past, ongoing, or potential real-world experience on FAIR

• Introducing new strategies for democratizing AI and making it affordable in low R&D countries and elsewhere

• Driving of Artificial Intelligence “AI” in the healthcare of the future societies, and the emerging debates on the democratization of ethical and FAIR AI

• Democratizing open data in low R&D countries: collection and sharing policies, security, acquisition protocols, etc

• Making AI affordable for healthcare and making Healthcare affordable with AI

We will compile all white papers into digital proceedings (PDF) which will be published on our FAIR-MICCAI website from year to year including all past editions. This will allow the MICCAI community to strive in identifying challenging topics in affordable AI in limited resources settings globally and address recurring issues through our archives.